A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images

PubMed Central

Hou, Bin; Wang, Yunhong; Liu, Qingjie

2016-01-01

Characterizations of up to date information of the Earth’s surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD) methods have been developed to solve them by utilizing remote sensing (RS) images. The advent of high resolution (HR) remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs) allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC) segmentation. Then, saliency and morphological building index (MBI) extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF). Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation. PMID:27618903

A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images.

PubMed

Hou, Bin; Wang, Yunhong; Liu, Qingjie

2016-08-27

Characterizations of up to date information of the Earth's surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD) methods have been developed to solve them by utilizing remote sensing (RS) images. The advent of high resolution (HR) remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs) allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC) segmentation. Then, saliency and morphological building index (MBI) extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF). Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation.

Continuous Change Detection of Urban Lakes in Wuhan, China Using Multi-Temporal Remote Sensing Images

NASA Astrophysics Data System (ADS)

Zhang, W.; Kong, X.; Tan, G.; Zheng, S.

2018-04-01

Urban lakes are important natural, scenic and pattern attractions of the city, and they are potential development resources as well. However, lots of urban lakes in China have been shrunk significantly or disappeared due to rapid urbanization. In this study, four Landsat images were used to perform a case study for lake change detection in downtown Wuhan, China, which were acquired on 1991, 2002, 2011 and 2017, respectively. Modified NDWI (MNDWI) was adopted to extract water bodies of urban areas from all these images, and OTSU was used to optimize the threshold selection. Furthermore, the variation of lake shrinkage was analysed in detail according to SVM classification and post-classification comparison, and the coverage of urban lakes in central area of Wuhan has decreased by 47.37 km2 between 1991 and 2017. The experimental results revealed that there were significant changes in the surface area of urban lakes over the 27 years, and it also indicated that rapid urbanization has a strong impact on the losses of urban water resources.

Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer’s disease

PubMed Central

Zhan, Liang; Zhou, Jiayu; Wang, Yalin; Jin, Yan; Jahanshad, Neda; Prasad, Gautam; Nir, Talia M.; Leonardo, Cassandra D.; Ye, Jieping; Thompson, Paul M.; for the Alzheimer’s Disease Neuroimaging Initiative

2015-01-01

Alzheimer’s disease (AD) involves a gradual breakdown of brain connectivity, and network analyses offer a promising new approach to track and understand disease progression. Even so, our ability to detect degenerative changes in brain networks depends on the methods used. Here we compared several tractography and feature extraction methods to see which ones gave best diagnostic classification for 202 people with AD, mild cognitive impairment or normal cognition, scanned with 41-gradient diffusion-weighted magnetic resonance imaging as part of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) project. We computed brain networks based on whole brain tractography with nine different methods – four of them tensor-based deterministic (FACT, RK2, SL, and TL), two orientation distribution function (ODF)-based deterministic (FACT, RK2), two ODF-based probabilistic approaches (Hough and PICo), and one “ball-and-stick” approach (Probtrackx). Brain networks derived from different tractography algorithms did not differ in terms of classification performance on ADNI, but performing principal components analysis on networks helped classification in some cases. Small differences may still be detectable in a truly vast cohort, but these experiments help assess the relative advantages of different tractography algorithms, and different post-processing choices, when used for classification. PMID:25926791

Mapping deforestation and urban expansion in Freetown, Sierra Leone, from pre- to post-war economic recovery.

PubMed

Mansaray, Lamin R; Huang, Jingfeng; Kamara, Alimamy A

2016-08-01

Freetown, the capital of Sierra Leone has experienced vast land-cover changes over the past three decades. In Sierra Leone, however, availability of updated land-cover data is still a problem even for environmental managers. This study was therefore, conducted to provide up-to-date land-cover data for Freetown. Multi-temporal Landsat data at 1986, 2001, and 2015 were obtained, and a maximum likelihood supervised classification was employed. Eight land-cover classes or categories were recognized as follows: water, wetland, built-up, dense forest, sparse forest, grassland, barren, and mangrove. Land-cover changes were mapped via post-classification change detection. The persistence, gain, and loss of each land-cover class, and selected land conversions were also quantified. An overall classification accuracy of 87.3 % and a Kappa statistic of 0.85 were obtained for the 2015 map. From 1986 to 2015, water, built-up, grassland, and barren had net gains, whereas forests, wetlands, and mangrove had net loses. Conversion analyses among forests, grassland, and built-up show that built-up had targeted grassland and avoided forests. This study also revealed that, the overall land-cover change at 2001-2015 was higher (28.5 %) than that recorded at 1986-2001 (20.9 %). This is attributable to the population increase in Freetown and the high economic growth and infrastructural development recorded countrywide after the civil war. In view of the rapid land-cover change and its associated environmental impacts, this study recommends the enactment of policies that would strike a balance between urbanization and environmental sustainability in Freetown.

Burn severity estimation using GeoEye imagery, object-based image analysis (OBIA), and Composite Burn Index (CBI) measurements

NASA Astrophysics Data System (ADS)

Dragozi, E.; Gitas, Ioannis Z.; Stavrakoudis, Dimitris G.; Minakou, C.

2015-06-01

Forest fires greatly influence the stability and functions of the forest ecosystems. The ever increasing need for accurate and detailed information regarding post-fire effects (burn severity) has led to several studies on the matter. In this study the combined use of Very High Resolution (VHR) satellite data (GeoEye), Objectbased image analysis (OBIA) and Composite Burn Index (CBI) measurements in estimating burn severity, at two different time points (2011 and 2012) is assessed. The accuracy of the produced maps was assessed and changes in burn severity between the two dates were detected using the post classification comparison approach. It was found that the produced burn severity map for 2011 was approximately 10% more accurate than that of 2012. This was mainly attributed to the increased heterogeneity of the study area in the second year, which led to an increased number of mixed class objects and consequently made it more difficult to spectrally discriminate between the severity classes. Following the post-classification analysis, the severity class changes were mainly attributed to the trees' ability to survive severe fire damage and sprout new leaves. Moreover, the results of the study suggest that when classifying CBI-based burn severity using VHR imagery it would be preferable to use images captured soon after the fire.

Detection of changes in semi-natural grasslands by cross correlation analysis with WorldView-2 images and new Landsat 8 data.

PubMed

Tarantino, Cristina; Adamo, Maria; Lucas, Richard; Blonda, Palma

2016-03-15

Focusing on a Mediterranean Natura 2000 site in Italy, the effectiveness of the cross correlation analysis (CCA) technique for quantifying change in the area of semi-natural grasslands at different spatial resolutions (grain) was evaluated. In a fine scale analysis (2 m), inputs to the CCA were a) a semi-natural grasslands layer extracted from an existing validated land cover/land use (LC/LU) map (1:5000, time T 1 ) and b) a more recent single date very high resolution (VHR) WorldView-2 image (time T 2 ), with T 2  > T 1 . The changes identified through the CCA were compared against those detected by applying a traditional post-classification comparison (PCC) technique to the same reference T 1 map and an updated T 2 map obtained by a knowledge driven classification of four multi-seasonal Worldview-2 input images. Specific changes observed were those associated with agricultural intensification and fires. The study concluded that prior knowledge (spectral class signatures, awareness of local agricultural practices and pressures) was needed for the selection of the most appropriate image (in terms of seasonality) to be acquired at T 2 . CCA was also applied to the comparison of the existing T 1 map with recent high resolution (HR) Landsat 8 OLS images. The areas of change detected at VHR and HR were broadly similar with larger error values in HR change images.

Using classification and NDVI differencing methods for monitoring sparse vegetation coverage: a case study of saltcedar in Nevada, USA.

USDA-ARS?s Scientific Manuscript database

A change detection experiment for an invasive species, saltcedar, near Lovelock, Nevada, was conducted with multi-date Compact Airborne Spectrographic Imager (CASI) hyperspectral datasets. Classification and NDVI differencing change detection methods were tested, In the classification strategy, a p...

Monitoring urban expansion and land use/land cover changes of Shanghai metropolitan area during the transitional economy (1979-2009) in China.

PubMed

Yin, Jie; Yin, Zhane; Zhong, Haidong; Xu, Shiyuan; Hu, Xiaomeng; Wang, Jun; Wu, Jianping

2011-06-01

This study explored the spatio-temporal dynamics and evolution of land use/cover changes and urban expansion in Shanghai metropolitan area, China, during the transitional economy period (1979-2009) using multi-temporal satellite images and geographic information systems (GIS). A maximum likelihood supervised classification algorithm was employed to extract information from four landsat images, with the post-classification change detection technique and GIS-based spatial analysis methods used to detect land-use and land-cover (LULC) changes. The overall Kappa indices of land use/cover change maps ranged from 0.79 to 0.89. Results indicated that urbanization has accelerated at an unprecedented scale and rate during the study period, leading to a considerable reduction in the area of farmland and green land. Findings further revealed that water bodies and bare land increased, obviously due to large-scale coastal development after 2000. The direction of urban expansion was along a north-south axis from 1979 to 2000, but after 2000 this growth changed to spread from both the existing urban area and along transport routes in all directions. Urban expansion and subsequent LULC changes in Shanghai have largely been driven by policy reform, population growth, and economic development. Rapid urban expansion through clearing of vegetation has led to a wide range of eco-environmental degradation.

Spatiotemporal analysis of land use and land cover change in the Brazilian Amazon

PubMed Central

Li, Guiying; Moran, Emilio; Hetrick, Scott

2013-01-01

This paper provides a comparative analysis of land use and land cover (LULC) changes among three study areas with different biophysical environments in the Brazilian Amazon at multiple scales, from per-pixel, polygon, census sector, to study area. Landsat images acquired in the years of 1990/1991, 1999/2000, and 2008/2010 were used to examine LULC change trajectories with the post-classification comparison approach. A classification system composed of six classes – forest, savanna, other-vegetation (secondary succession and plantations), agro-pasture, impervious surface, and water, was designed for this study. A hierarchical-based classification method was used to classify Landsat images into thematic maps. This research shows different spatiotemporal change patterns, composition and rates among the three study areas and indicates the importance of analyzing LULC change at multiple scales. The LULC change analysis over time for entire study areas provides an overall picture of change trends, but detailed change trajectories and their spatial distributions can be better examined at a per-pixel scale. The LULC change at the polygon scale provides the information of the changes in patch sizes over time, while the LULC change at census sector scale gives new insights on how human-induced activities (e.g., urban expansion, roads, and land use history) affect LULC change patterns and rates. This research indicates the necessity to implement change detection at multiple scales for better understanding the mechanisms of LULC change patterns and rates. PMID:24127130

Image change detection using paradoxical theory for patient follow-up quantitation and therapy assessment.

PubMed

David, Simon; Visvikis, Dimitris; Quellec, Gwénolé; Le Rest, Catherine Cheze; Fernandez, Philippe; Allard, Michèle; Roux, Christian; Hatt, Mathieu

2012-09-01

In clinical oncology, positron emission tomography (PET) imaging can be used to assess therapeutic response by quantifying the evolution of semi-quantitative values such as standardized uptake value, early during treatment or after treatment. Current guidelines do not include metabolically active tumor volume (MATV) measurements and derived parameters such as total lesion glycolysis (TLG) to characterize the response to the treatment. To achieve automatic MATV variation estimation during treatment, we propose an approach based on the change detection principle using the recent paradoxical theory, which models imprecision, uncertainty, and conflict between sources. It was applied here simultaneously to pre- and post-treatment PET scans. The proposed method was applied to both simulated and clinical datasets, and its performance was compared to adaptive thresholding applied separately on pre- and post-treatment PET scans. On simulated datasets, the adaptive threshold was associated with significantly higher classification errors than the developed approach. On clinical datasets, the proposed method led to results more consistent with the known partial responder status of these patients. The method requires accurate rigid registration of both scans which can be obtained only in specific body regions and does not explicitly model uptake heterogeneity. In further investigations, the change detection of intra-MATV tracer uptake heterogeneity will be developed by incorporating textural features into the proposed approach.

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.

Land development in Massachusetts: Its effect on the environment within Essex and Middlesex counties from 1990 to 2007

NASA Astrophysics Data System (ADS)

Tardie, Peter Sean

Since the 1970's urban centers in and surrounding Essex and Middlesex Counties in Massachusetts have expanded and proliferated into adjacent communities. This expansion has led to the conversion of land for housing, businesses, schools, recreation, and parks, placing significant strain on existing land cover, land use, and available natural resources. Mounting growth pressures and a reduction of undeveloped land have raised serious concerns as cropland and forest fragmentation, wetland destruction, protected open-space infringement, pollution, and systematic losses of rural conditions have become obvious. To monitor development, the post-classification change detection method was applied to Landsat Thematic Mapper (TM) satellite data and GIS was used to detect, quantity, and document the extent of development and its effect on the environment and to assess and quantify the demographic changes that occurred within the counties from 1990 to 2007. Classification of the 1990 image resulted in 217 clusters and 214 clusters for the 2007 image The overall accuracy achieved for the 1990 image classification was 87.3% with a KHAT value of 0.848, and the overall accuracy for the 2007 classification was 86.27% with a KHAT value of 0.840. From 1990 to 2007 land cover change occurred primarily along major transportation corridors. The post-classification change detection results indicate that Essex and Middlesex County combined gained 23,435.66 "new" acres of land development from 1990 to 2007 through a loss and change in acreage from the Bareland, Forest, Grassland, Water, and Wetland land cover class categories. Results indicate that there was an approximate 0.56% overall (net) increase of newly developed land areas within the 1990 and 2007 image classifications from 415.46 acres or 0.64 square miles. In addition, there was a substantial decrease (-40.0%) within the grassland category. Land development was responsible for a portion of the decrease of grasslands (-13.63%), which occurred mostly within Middlesex County. Results also indicate that "new" land development occurred within several Commonwealth of Massachusetts designated environmentally-sensitive areas: 722 acres in areas of critical environmental concern, 670 acres in priority habitats of rare species, 1,092 acres in living waters core habitats and critical supporting watersheds, 1,318 acres in protected and recreational open spaces, and within 0-1000 feet of 600 certified vernal pools. In addition, several rare or imperiled species inhabiting these areas may have been adversely affected by land development through habitat loss, change, or fragmentation, and/or passage corridor disruptions. A GIS comparison of the "new" land development acreages and census demographic statistics within Essex and Middlesex County cities and towns during this period indicate that communities with more families with children exhibited more land development, and communities with higher median household income exhibited less land development. Land change detection over the 17-year period indicated encroachment of development in areas of environmental concern, but level of development varied by socio-demographic factors. This study also illustrated that the combined use of remotely sensed data, Geographic Information Systems (GIS) technology, and demographic data are effective for use as a diagnostic tool and/or base to be built upon to explore associations, indicators, or drivers which may influence land cover change and its effects on existing environmental conditions in areas exhibiting change. In addition, this study provided awareness to ancillary research where scientific guidelines were derived for the protection of specific wildlife habitats and resident species. Lastly, this study presented several land cover modeling and web deployed data dissemination tools for the dissertation results as well as provided a conceptual framework for the successful adoption and implementation of these tools for organizations engaged in natural resource planning and management.

A General Framework for Discovery and Classification in Astronomy

NASA Astrophysics Data System (ADS)

Dick, Steven J.

2012-09-01

An analysis of the discovery of 82 classes of astronomical objects reveals an extended structure of discovery, consisting of detection, interpretation and understanding, each with its own nuances and a microstructure including conceptual, technological and social roles. This is true with a remarkable degree of consistency over the last 400 years of telescopic astronomy, ranging from Galileo's discovery of satellites, planetary rings and star clusters, to the discovery of quasars and pulsars. Telescopes have served as ``engines of discovery'' in several ways, ranging from telescope size and sensitivity (planetary nebulae and spiral nebulae), to specialized detectors (TNOs) and the opening of the electromagnetic spectrum for astronomy (pulsars, pulsar planets, and most active galaxies). A few classes (radiation belts, the solar wind and cosmic rays) were initially discovered without the telescope. Classification also plays an important role in discovery. While it might seem that classification marks the end of discovery, or a post-discovery phase, in fact it often marks the beginning, even a pre-discovery phase. Nowhere is this more clearly seen than in the classification of stellar spectra, long before dwarfs, giants and supergiants were known, or their evolutionary sequence recognized. Classification may also be part of a post-discovery phase, as in the MK system of stellar classification, constructed after the discovery of stellar luminosity classes. Some classes are declared rather than detected, as in the case of gas and ice giant planets, and, infamously, Pluto as a dwarf planet. Others are inferred rather than detected, including most classes of stars.

Using remote sensing and GIS to detect and monitor land use and land cover change in Dhaka Metropolitan of Bangladesh during 1960-2005.

PubMed

Dewan, Ashraf M; Yamaguchi, Yasushi

2009-03-01

This paper illustrates the result of land use/cover change in Dhaka Metropolitan of Bangladesh using topographic maps and multi-temporal remotely sensed data from 1960 to 2005. The Maximum likelihood supervised classification technique was used to extract information from satellite data, and post-classification change detection method was employed to detect and monitor land use/cover change. Derived land use/cover maps were further validated by using high resolution images such as SPOT, IRS, IKONOS and field data. The overall accuracy of land cover change maps, generated from Landsat and IRS-1D data, ranged from 85% to 90%. The analysis indicated that the urban expansion of Dhaka Metropolitan resulted in the considerable reduction of wetlands, cultivated land, vegetation and water bodies. The maps showed that between 1960 and 2005 built-up areas increased approximately 15,924 ha, while agricultural land decreased 7,614 ha, vegetation decreased 2,336 ha, wetland/lowland decreased 6,385 ha, and water bodies decreased about 864 ha. The amount of urban land increased from 11% (in 1960) to 344% in 2005. Similarly, the growth of landfill/bare soils category was about 256% in the same period. Much of the city's rapid growth in population has been accommodated in informal settlements with little attempt being made to limit the risk of environmental impairments. The study quantified the patterns of land use/cover change for the last 45 years for Dhaka Metropolitan that forms valuable resources for urban planners and decision makers to devise sustainable land use and environmental planning.

Change in Autism Classification with Early Intervention: Predictors and Outcomes

ERIC Educational Resources Information Center

Ben Itzchak, Esther; Zachor, Ditza A.

2009-01-01

The current study characterized stability and changes of autism diagnostic classification with intervention in very young children and examined pre-treatment predictors and post-intervention outcome. Sixty-eight children diagnosed with autism, aged 18-35 months (M = 25.4, SD = 4.0) participated in the study. Children underwent comprehensive…

Feasibility of Multispectral Airborne Laser Scanning for Land Cover Classification, Road Mapping and Map Updating

NASA Astrophysics Data System (ADS)

Matikainen, L.; Karila, K.; Hyyppä, J.; Puttonen, E.; Litkey, P.; Ahokas, E.

2017-10-01

This article summarises our first results and experiences on the use of multispectral airborne laser scanner (ALS) data. Optech Titan multispectral ALS data over a large suburban area in Finland were acquired on three different dates in 2015-2016. We investigated the feasibility of the data from the first date for land cover classification and road mapping. Object-based analyses with segmentation and random forests classification were used. The potential of the data for change detection of buildings and roads was also demonstrated. The overall accuracy of land cover classification results with six classes was 96 % compared with validation points. The data also showed high potential for road detection, road surface classification and change detection. The multispectral intensity information appeared to be very important for automated classifications. Compared to passive aerial images, the intensity images have interesting advantages, such as the lack of shadows. Currently, we focus on analyses and applications with the multitemporal multispectral data. Important questions include, for example, the potential and challenges of the multitemporal data for change detection.

Updating Landsat-derived land-cover maps using change detection and masking techniques

NASA Technical Reports Server (NTRS)

Likens, W.; Maw, K.

1982-01-01

The California Integrated Remote Sensing System's San Bernardino County Project was devised to study the utilization of a data base at a number of jurisdictional levels. The present paper discusses the implementation of change-detection and masking techniques in the updating of Landsat-derived land-cover maps. A baseline landcover classification was first created from a 1976 image, then the adjusted 1976 image was compared with a 1979 scene by the techniques of (1) multidate image classification, (2) difference image-distribution tails thresholding, (3) difference image classification, and (4) multi-dimensional chi-square analysis of a difference image. The union of the results of methods 1, 3 and 4 was used to create a mask of possible change areas between 1976 and 1979, which served to limit analysis of the update image and reduce comparison errors in unchanged areas. The techniques of spatial smoothing of change-detection products, and of combining results of difference change-detection algorithms are also shown to improve Landsat change-detection accuracies.

Seafloor change detection using multibeam echosounder backscatter: case study on the Belgian part of the North Sea

NASA Astrophysics Data System (ADS)

Montereale-Gavazzi, Giacomo; Roche, Marc; Lurton, Xavier; Degrendele, Koen; Terseleer, Nathan; Van Lancker, Vera

2018-06-01

To characterize seafloor substrate type, seabed mapping and particularly multibeam echosounding are increasingly used. Yet, the utilisation of repetitive MBES-borne backscatter surveys to monitor the environmental status of the seafloor remains limited. Often methodological frameworks are missing, and should comprise of a suite of change detection procedures, similarly to those developed in the terrestrial sciences. In this study, pre-, ensemble and post-classification approaches were tested on an eight km2 study site within a Habitat Directive Area in the Belgian part of the North Sea. In this area, gravel beds with epifaunal assemblages were observed. Flourishing of the fauna is constrained by overtopping with sand or increased turbidity levels, which could result from anthropogenic activities. Monitoring of the gravel to sand ratio was hence put forward as an indicator of good environmental status. Seven acoustic surveys were undertaken from 2004 to 2015. The methods allowed quantifying temporal trends and patterns of change of the main substrate classes identified in the study area; namely fine to medium homogenous sand, medium sand with bioclastic detritus and medium to coarse sand with gravel. Results indicated that by considering the entire study area and the entire time series, the gravel to sand ratio fluctuated, but was overall stable. Nonetheless, when only the biodiversity hotspots were considered, net losses and a gradual trend, indicative of potential smothering, was captured by ensemble and post-classification approaches respectively. Additionally, a two-dimensional morphological analysis, based on the bathymetric data, suggested a loss of profile complexity from 2004 to 2015. Causal relationships with natural and anthropogenic stressors are yet to be established. The methodologies presented and discussed are repeatable and can be applied to broad-scale geographical extents given that broad-scale time series datasets become available.

Spectral-Based Volume Sensor Prototype, Post-VS4 Test Series Algorithm Development

DTIC Science & Technology

2009-04-30

Computer Pcorr Probabilty / Percentage of Correct Classification (# Correct / # Total) PD PhotoDiode Pd Probabilty / Percentage of Detection (# Correct...Detections / Total of Sources) Pfa Probabilty / Percentage of False Alarm (# FAs / Total # of Sources) SBVS Spectral-Based Volume Sensor SFA Smoke and

TwitterSensing: An Event-Based Approach for Wireless Sensor Networks Optimization Exploiting Social Media in Smart City Applications

PubMed Central

2018-01-01

Modern cities are subject to periodic or unexpected critical events, which may bring economic losses or even put people in danger. When some monitoring systems based on wireless sensor networks are deployed, sensing and transmission configurations of sensor nodes may be adjusted exploiting the relevance of the considered events, but efficient detection and classification of events of interest may be hard to achieve. In Smart City environments, several people spontaneously post information in social media about some event that is being observed and such information may be mined and processed for detection and classification of critical events. This article proposes an integrated approach to detect and classify events of interest posted in social media, notably in Twitter, and the assignment of sensing priorities to source nodes. By doing so, wireless sensor networks deployed in Smart City scenarios can be optimized for higher efficiency when monitoring areas under the influence of the detected events. PMID:29614060

TwitterSensing: An Event-Based Approach for Wireless Sensor Networks Optimization Exploiting Social Media in Smart City Applications.

PubMed

Costa, Daniel G; Duran-Faundez, Cristian; Andrade, Daniel C; Rocha-Junior, João B; Peixoto, João Paulo Just

2018-04-03

Modern cities are subject to periodic or unexpected critical events, which may bring economic losses or even put people in danger. When some monitoring systems based on wireless sensor networks are deployed, sensing and transmission configurations of sensor nodes may be adjusted exploiting the relevance of the considered events, but efficient detection and classification of events of interest may be hard to achieve. In Smart City environments, several people spontaneously post information in social media about some event that is being observed and such information may be mined and processed for detection and classification of critical events. This article proposes an integrated approach to detect and classify events of interest posted in social media, notably in Twitter , and the assignment of sensing priorities to source nodes. By doing so, wireless sensor networks deployed in Smart City scenarios can be optimized for higher efficiency when monitoring areas under the influence of the detected events.

Visual long-term memory and change blindness: Different effects of pre- and post-change information on one-shot change detection using meaningless geometric objects.

PubMed

Nishiyama, Megumi; Kawaguchi, Jun

2014-11-01

To clarify the relationship between visual long-term memory (VLTM) and online visual processing, we investigated whether and how VLTM involuntarily affects the performance of a one-shot change detection task using images consisting of six meaningless geometric objects. In the study phase, participants observed pre-change (Experiment 1), post-change (Experiment 2), or both pre- and post-change (Experiment 3) images appearing in the subsequent change detection phase. In the change detection phase, one object always changed between pre- and post-change images and participants reported which object was changed. Results showed that VLTM of pre-change images enhanced the performance of change detection, while that of post-change images decreased accuracy. Prior exposure to both pre- and post-change images did not influence performance. These results indicate that pre-change information plays an important role in change detection, and that information in VLTM related to the current task does not always have a positive effect on performance. Copyright © 2014 Elsevier Inc. All rights reserved.

Subsurface event detection and classification using Wireless Signal Networks.

PubMed

Yoon, Suk-Un; Ghazanfari, Ehsan; Cheng, Liang; Pamukcu, Sibel; Suleiman, Muhannad T

2012-11-05

Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs). The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events.

Subsurface Event Detection and Classification Using Wireless Signal Networks

PubMed Central

Yoon, Suk-Un; Ghazanfari, Ehsan; Cheng, Liang; Pamukcu, Sibel; Suleiman, Muhannad T.

2012-01-01

Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs). The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events. PMID:23202191

Multitemporal spatial pattern analysis of Tulum's tropical coastal landscape

NASA Astrophysics Data System (ADS)

Ramírez-Forero, Sandra Carolina; López-Caloca, Alejandra; Silván-Cárdenas, José Luis

2011-11-01

The tropical coastal landscape of Tulum in Quintana Roo, Mexico has a high ecological, economical, social and cultural value, it provides environmental and tourism services at global, national, regional and local levels. The landscape of the area is heterogeneous and presents random fragmentation patterns. In recent years, tourist services of the region has been increased promoting an accelerate expansion of hotels, transportation and recreation infrastructure altering the complex landscape. It is important to understand the environmental dynamics through temporal changes on the spatial patterns and to propose a better management of this ecological area to the authorities. This paper addresses a multi-temporal analysis of land cover changes from 1993 to 2000 in Tulum using Thematic Mapper data acquired by Landsat-5. Two independent methodologies were applied for the analysis of changes in the landscape and for the definition of fragmentation patterns. First, an Iteratively Multivariate Alteration Detection (IR-MAD) algorithm was used to detect and localize land cover change/no-change areas. Second, the post-classification change detection evaluated using the Support Vector Machine (SVM) algorithm. Landscape metrics were calculated from the results of IR-MAD and SVM. The analysis of the metrics indicated, among other things, a higher fragmentation pattern along roadways.

Inconsistent classification of pusher behaviour in stroke patients: a direct comparison of the Scale for Contraversive Pushing and the Burke Lateropulsion Scale.

PubMed

Bergmann, Jeannine; Krewer, Carmen; Rieß, Katrin; Müller, Friedemann; Koenig, Eberhard; Jahn, Klaus

2014-07-01

To compare the classification of two clinical scales for assessing pusher behaviour in a cohort of stroke patients. Observational case-control study. Inpatient stroke rehabilitation unit. A sample of 23 patients with hemiparesis due to a unilateral stroke (1.6 ± 0.7 months post stroke). Immediately before and after three different interventions, the Scale for Contraversive Pushing and the Burke Lateropulsion Scale were applied in a standardized procedure. The diagnosis of pusher behaviour on the basis of the Scale for Contraversive Pushing and the Burke Lateropulsion Scale differed significantly (χ2 = 54.260, p < 0.001) resulting in inconsistent classifications in 31 of 138 cases. Changes immediately after the interventions were more often detected by the Burke Lateropulsion Scales than by the Scale for Contraversive Pushing (χ2 = 19.148, p < 0.001). All cases with inconsistent classifications showed no pusher behaviour on the Scale for Contraversive Pushing, but pusher behaviour on the Burke Lateropulsion Scale. 64.5% (20 of 31) of them scored on the Burke Lateropulsion Scale on the standing and walking items only. The Burke Lateropulsion Scale is an appropriate alternative to the widely used Scale for Contraversive Pushing to follow-up patients with pusher behaviour (PB); it might be more sensitive to detect mild pusher behaviour in standing and walking. © The Author(s) 2014.

Detecting glaucomatous change in visual fields: Analysis with an optimization framework.

PubMed

Yousefi, Siamak; Goldbaum, Michael H; Varnousfaderani, Ehsan S; Belghith, Akram; Jung, Tzyy-Ping; Medeiros, Felipe A; Zangwill, Linda M; Weinreb, Robert N; Liebmann, Jeffrey M; Girkin, Christopher A; Bowd, Christopher

2015-12-01

Detecting glaucomatous progression is an important aspect of glaucoma management. The assessment of longitudinal series of visual fields, measured using Standard Automated Perimetry (SAP), is considered the reference standard for this effort. We seek efficient techniques for determining progression from longitudinal visual fields by formulating the problem as an optimization framework, learned from a population of glaucoma data. The longitudinal data from each patient's eye were used in a convex optimization framework to find a vector that is representative of the progression direction of the sample population, as a whole. Post-hoc analysis of longitudinal visual fields across the derived vector led to optimal progression (change) detection. The proposed method was compared to recently described progression detection methods and to linear regression of instrument-defined global indices, and showed slightly higher sensitivities at the highest specificities than other methods (a clinically desirable result). The proposed approach is simpler, faster, and more efficient for detecting glaucomatous changes, compared to our previously proposed machine learning-based methods, although it provides somewhat less information. This approach has potential application in glaucoma clinics for patient monitoring and in research centers for classification of study participants. Copyright © 2015 Elsevier Inc. All rights reserved.

Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction.

PubMed

Faust, Kevin; Xie, Quin; Han, Dominick; Goyle, Kartikay; Volynskaya, Zoya; Djuric, Ugljesa; Diamandis, Phedias

2018-05-16

There is growing interest in utilizing artificial intelligence, and particularly deep learning, for computer vision in histopathology. While accumulating studies highlight expert-level performance of convolutional neural networks (CNNs) on focused classification tasks, most studies rely on probability distribution scores with empirically defined cutoff values based on post-hoc analysis. More generalizable tools that allow humans to visualize histology-based deep learning inferences and decision making are scarce. Here, we leverage t-distributed Stochastic Neighbor Embedding (t-SNE) to reduce dimensionality and depict how CNNs organize histomorphologic information. Unique to our workflow, we develop a quantitative and transparent approach to visualizing classification decisions prior to softmax compression. By discretizing the relationships between classes on the t-SNE plot, we show we can super-impose randomly sampled regions of test images and use their distribution to render statistically-driven classifications. Therefore, in addition to providing intuitive outputs for human review, this visual approach can carry out automated and objective multi-class classifications similar to more traditional and less-transparent categorical probability distribution scores. Importantly, this novel classification approach is driven by a priori statistically defined cutoffs. It therefore serves as a generalizable classification and anomaly detection tool less reliant on post-hoc tuning. Routine incorporation of this convenient approach for quantitative visualization and error reduction in histopathology aims to accelerate early adoption of CNNs into generalized real-world applications where unanticipated and previously untrained classes are often encountered.

Adding an alcohol-related risk score to an existing categorical risk classification for older adults: sensitivity to group differences.

PubMed

Wilson, Sandra R; Fink, Arlene; Verghese, Shinu; Beck, John C; Nguyen, Khue; Lavori, Philip

2007-03-01

To evaluate a new alcohol-related risk score for research use. Using data from a previously reported trial of a screening and education system for older adults (Computerized Alcohol-Related Problems Survey), secondary analyses were conducted comparing the ability of two different measures of risk to detect post-intervention group differences: the original categorical outcome measure and a new, finely grained quantitative risk score based on the same research-based risk factors. Three primary care group practices in southern California. Six hundred sixty-five patients aged 65 and older. A previously calculated, three-level categorical classification of alcohol-related risk and a newly developed quantitative risk score. Mean post-intervention risk scores differed between the three experimental conditions: usual care, patient report, and combined report (P<.001). The difference between the combined report and usual care was significant (P<.001) and directly proportional to baseline risk. The three-level risk classification did not reveal approximately 57.3% of the intervention effect detected by the risk score. The risk score also was sufficiently sensitive to detect the intervention effect within the subset of hypertensive patients (n=112; P=.001). As an outcome measure in intervention trials, the finely grained risk score is more sensitive than the trinary risk classification. The additional clinical value of the risk score relative to the categorical measure needs to be determined.

Continuous Change Detection and Classification (CCDC) of Land Cover Using All Available Landsat Data

NASA Astrophysics Data System (ADS)

Zhu, Z.; Woodcock, C. E.

2012-12-01

A new algorithm for Continuous Change Detection and Classification (CCDC) of land cover using all available Landsat data is developed. This new algorithm is capable of detecting many kinds of land cover change as new images are collected and at the same time provide land cover maps for any given time. To better identify land cover change, a two step cloud, cloud shadow, and snow masking algorithm is used for eliminating "noisy" observations. Next, a time series model that has components of seasonality, trend, and break estimates the surface reflectance and temperature. The time series model is updated continuously with newly acquired observations. Due to the high variability in spectral response for different kinds of land cover change, the CCDC algorithm uses a data-driven threshold derived from all seven Landsat bands. When the difference between observed and predicted exceeds the thresholds three consecutive times, a pixel is identified as land cover change. Land cover classification is done after change detection. Coefficients from the time series models and the Root Mean Square Error (RMSE) from model fitting are used as classification inputs for the Random Forest Classifier (RFC). We applied this new algorithm for one Landsat scene (Path 12 Row 31) that includes all of Rhode Island as well as much of Eastern Massachusetts and parts of Connecticut. A total of 532 Landsat images acquired between 1982 and 2011 were processed. During this period, 619,924 pixels were detected to change once (91% of total changed pixels) and 60,199 pixels were detected to change twice (8% of total changed pixels). The most frequent land cover change category is from mixed forest to low density residential which occupies more than 8% of total land cover change pixels.

Portable automatic text classification for adverse drug reaction detection via multi-corpus training.

PubMed

Sarker, Abeed; Gonzalez, Graciela

2015-02-01

Automatic detection of adverse drug reaction (ADR) mentions from text has recently received significant interest in pharmacovigilance research. Current research focuses on various sources of text-based information, including social media-where enormous amounts of user posted data is available, which have the potential for use in pharmacovigilance if collected and filtered accurately. The aims of this study are: (i) to explore natural language processing (NLP) approaches for generating useful features from text, and utilizing them in optimized machine learning algorithms for automatic classification of ADR assertive text segments; (ii) to present two data sets that we prepared for the task of ADR detection from user posted internet data; and (iii) to investigate if combining training data from distinct corpora can improve automatic classification accuracies. One of our three data sets contains annotated sentences from clinical reports, and the two other data sets, built in-house, consist of annotated posts from social media. Our text classification approach relies on generating a large set of features, representing semantic properties (e.g., sentiment, polarity, and topic), from short text nuggets. Importantly, using our expanded feature sets, we combine training data from different corpora in attempts to boost classification accuracies. Our feature-rich classification approach performs significantly better than previously published approaches with ADR class F-scores of 0.812 (previously reported best: 0.770), 0.538 and 0.678 for the three data sets. Combining training data from multiple compatible corpora further improves the ADR F-scores for the in-house data sets to 0.597 (improvement of 5.9 units) and 0.704 (improvement of 2.6 units) respectively. Our research results indicate that using advanced NLP techniques for generating information rich features from text can significantly improve classification accuracies over existing benchmarks. Our experiments illustrate the benefits of incorporating various semantic features such as topics, concepts, sentiments, and polarities. Finally, we show that integration of information from compatible corpora can significantly improve classification performance. This form of multi-corpus training may be particularly useful in cases where data sets are heavily imbalanced (e.g., social media data), and may reduce the time and costs associated with the annotation of data in the future. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Portable Automatic Text Classification for Adverse Drug Reaction Detection via Multi-corpus Training

PubMed Central

Gonzalez, Graciela

2014-01-01

Objective Automatic detection of Adverse Drug Reaction (ADR) mentions from text has recently received significant interest in pharmacovigilance research. Current research focuses on various sources of text-based information, including social media — where enormous amounts of user posted data is available, which have the potential for use in pharmacovigilance if collected and filtered accurately. The aims of this study are: (i) to explore natural language processing approaches for generating useful features from text, and utilizing them in optimized machine learning algorithms for automatic classification of ADR assertive text segments; (ii) to present two data sets that we prepared for the task of ADR detection from user posted internet data; and (iii) to investigate if combining training data from distinct corpora can improve automatic classification accuracies. Methods One of our three data sets contains annotated sentences from clinical reports, and the two other data sets, built in-house, consist of annotated posts from social media. Our text classification approach relies on generating a large set of features, representing semantic properties (e.g., sentiment, polarity, and topic), from short text nuggets. Importantly, using our expanded feature sets, we combine training data from different corpora in attempts to boost classification accuracies. Results Our feature-rich classification approach performs significantly better than previously published approaches with ADR class F-scores of 0.812 (previously reported best: 0.770), 0.538 and 0.678 for the three data sets. Combining training data from multiple compatible corpora further improves the ADR F-scores for the in-house data sets to 0.597 (improvement of 5.9 units) and 0.704 (improvement of 2.6 units) respectively. Conclusions Our research results indicate that using advanced NLP techniques for generating information rich features from text can significantly improve classification accuracies over existing benchmarks. Our experiments illustrate the benefits of incorporating various semantic features such as topics, concepts, sentiments, and polarities. Finally, we show that integration of information from compatible corpora can significantly improve classification performance. This form of multi-corpus training may be particularly useful in cases where data sets are heavily imbalanced (e.g., social media data), and may reduce the time and costs associated with the annotation of data in the future. PMID:25451103

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

Improving the precision of dynamic forest parameter estimates using Landsat

Treesearch

Evan B. Brooks; John W. Coulston; Randolph H. Wynne; Valerie A. Thomas

2016-01-01

The use of satellite-derived classification maps to improve post-stratified forest parameter estimates is wellestablished.When reducing the variance of post-stratification estimates for forest change parameters such as forestgrowth, it is logical to use a change-related strata map. At the stand level, a time series of Landsat images is

Detection of Coastline Deformation Using Remote Sensing and Geodetic Surveys

NASA Astrophysics Data System (ADS)

Sabuncu, A.; Dogru, A.; Ozener, H.; Turgut, B.

2016-06-01

The coastal areas are being destroyed due to the usage that effect the natural balance. Unconsciously sand mining from the sea for nearshore nourishment and construction uses are the main ones. Physical interferences for mining of sand cause an ecologic threat to the coastal environment. However, use of marine sand is inevitable because of economic reasons or unobtainable land-based sand resources. The most convenient solution in such a protection-usage dilemma is to reduce negative impacts of sand production from marine. This depends on the accurate determination of criteriaon production place, style, and amount of sand. With this motivation, nearshore geodedic surveying studies performed on Kilyos Campus of Bogazici University located on the Black Sea coast, north of Istanbul, Turkey between 2001-2002. The study area extends 1 km in the longshore. Geodetic survey was carried out in the summer of 2001 to detect the initial condition for the shoreline. Long-term seasonal changes in shoreline positions were determined biannually. The coast was measured with post-processed kinematic GPS. Besides, shoreline change has studied using Landsat imagery between the years 1986-2015. The data set of Landsat 5 imageries were dated 05.08.1986 and 31.08.2007 and Landsat 7 imageries were dated 21.07.2001 and 28.07.2015. Landcover types in the study area were analyzed on the basis of pixel based classification method. Firstly, unsupervised classification based on ISODATA (Iterative Self Organizing Data Analysis Technique) has been applied and spectral clusters have been determined that gives prior knowledge about the study area. In the second step, supervised classification was carried out by using the three different approaches which are minimum-distance, parallelepiped and maximum-likelihood. All pixel based classification processes were performed with ENVI 4.8 image processing software. Results of geodetic studies and classification outputs will be presented in this paper.

Urban land-cover change detection through sub-pixel imperviousness mapping using remotely sensed data

USGS Publications Warehouse

Yang, Limin; Xian, George Z.; Klaver, Jacqueline M.; Deal, Brian

2003-01-01

We developed a Sub-pixel Imperviousness Change Detection (SICD) approach to detect urban land-cover changes using Landsat and high-resolution imagery. The sub-pixel percent imperviousness was mapped for two dates (09 March 1993 and 11 March 2001) over western Georgia using a regression tree algorithm. The accuracy of the predicted imperviousness was reasonable based on a comparison using independent reference data. The average absolute error between predicted and reference data was 16.4 percent for 1993 and 15.3 percent for 2001. The correlation coefficient (r) was 0.73 for 1993 and 0.78 for 2001, respectively. Areas with a significant increase (greater than 20 percent) in impervious surface from 1993 to 2001 were mostly related to known land-cover/land-use changes that occurred in this area, suggesting that the spatial change of an impervious surface is a useful indicator for identifying spatial extent, intensity, and, potentially, type of urban land-cover/land-use changes. Compared to other pixel-based change-detection methods (band differencing, rationing, change vector, post-classification), information on changes in sub-pixel percent imperviousness allow users to quantify and interpret urban land-cover/land-use changes based on their own definition. Such information is considered complementary to products generated using other change-detection methods. In addition, the procedure for mapping imperviousness is objective and repeatable, hence, can be used for monitoring urban land-cover/land-use change over a large geographic area. Potential applications and limitations of the products developed through this study in urban environmental studies are also discussed.

Spatiotemporal Change Detection Using Landsat Imagery: the Case Study of Karacabey Flooded Forest, Bursa, Turkey

NASA Astrophysics Data System (ADS)

Akay, A. E.; Gencal, B.; Taş, İ.

2017-11-01

This short paper aims to detect spatiotemporal detection of land use/land cover change within Karacabey Flooded Forest region. Change detection analysis applied to Landsat 5 TM images representing July 2000 and a Landsat 8 OLI representing June 2017. Various image processing tools were implemented using ERDAS 9.2, ArcGIS 10.4.1, and ENVI programs to conduct spatiotemporal change detection over these two images such as band selection, corrections, subset, classification, recoding, accuracy assessment, and change detection analysis. Image classification revealed that there are five significant land use/land cover types, including forest, flooded forest, swamp, water, and other lands (i.e. agriculture, sand, roads, settlement, and open areas). The results indicated that there was increase in flooded forest, water, and other lands, while the cover of forest and swamp decreased.

Unsupervised change detection in a particular vegetation land cover type using spectral angle mapper

NASA Astrophysics Data System (ADS)

Renza, Diego; Martinez, Estibaliz; Molina, Iñigo; Ballesteros L., Dora M.

2017-04-01

This paper presents a new unsupervised change detection methodology for multispectral images applied to specific land covers. The proposed method involves comparing each image against a reference spectrum, where the reference spectrum is obtained from the spectral signature of the type of coverage you want to detect. In this case the method has been tested using multispectral images (SPOT5) of the community of Madrid (Spain), and multispectral images (Quickbird) of an area over Indonesia that was impacted by the December 26, 2004 tsunami; here, the tests have focused on the detection of changes in vegetation. The image comparison is obtained by applying Spectral Angle Mapper between the reference spectrum and each multitemporal image. Then, a threshold to produce a single image of change is applied, which corresponds to the vegetation zones. The results for each multitemporal image are combined through an exclusive or (XOR) operation that selects vegetation zones that have changed over time. Finally, the derived results were compared against a supervised method based on classification with the Support Vector Machine. Furthermore, the NDVI-differencing and the Spectral Angle Mapper techniques were selected as unsupervised methods for comparison purposes. The main novelty of the method consists in the detection of changes in a specific land cover type (vegetation), therefore, for comparison purposes, the best scenario is to compare it with methods that aim to detect changes in a specific land cover type (vegetation). This is the main reason to select NDVI-based method and the post-classification method (SVM implemented in a standard software tool). To evaluate the improvements using a reference spectrum vector, the results are compared with the basic-SAM method. In SPOT5 image, the overall accuracy was 99.36% and the κ index was 90.11%; in Quickbird image, the overall accuracy was 97.5% and the κ index was 82.16%. Finally, the precision results of the method are comparable to those of a supervised method, supported by low detection of false positives and false negatives, along with a high overall accuracy and a high kappa index. On the other hand, the execution times were comparable to those of unsupervised methods of low computational load.

Signal processing for non-destructive testing of railway tracks

NASA Astrophysics Data System (ADS)

Heckel, Thomas; Casperson, Ralf; Rühe, Sven; Mook, Gerhard

2018-04-01

Increased speed, heavier loads, altered material and modern drive systems result in an increasing number of rail flaws. The appearance of these flaws also changes continually due to the rapid change in damage mechanisms of modern rolling stock. Hence, interpretation has become difficult when evaluating non-destructive rail testing results. Due to the changed interplay between detection methods and flaws, the recorded signals may result in unclassified types of rail flaws. Methods for automatic rail inspection (according to defect detection and classification) undergo continual development. Signal processing is a key technology to master the challenge of classification and maintain resolution and detection quality, independent of operation speed. The basic ideas of signal processing, based on the Glassy-Rail-Diagram for classification purposes, are presented herein. Examples for the detection of damages caused by rolling contact fatigue also are given, and synergetic effects of combined evaluation of diverse inspection methods are shown.

Multi-scale investigation of shrub encroachment in southern Africa

NASA Astrophysics Data System (ADS)

Aplin, Paul; Marston, Christopher; Wilkinson, David; Field, Richard; O'Regan, Hannah

2016-04-01

There is growing speculation that savannah environments throughout Africa have been subject to shrub encroachment in recent years, whereby grassland is lost to woody vegetation cover. Changes in the relative proportions of grassland and woodland are important in the context of conservation of savannah systems, with implications for faunal distributions, environmental management and tourism. Here, we focus on southern Kruger National Park, South Africa, and investigate whether or not shrub encroachment has occurred over the last decade and a half. We use a multi-scale approach, examining the complementarity of medium (e.g. Landsat TM and OLI) and fine (e.g. QuickBird and WorldView-2) spatial resolution satellite sensor imagery, supported by intensive field survey in 2002 and 2014. We employ semi-automated land cover classification, involving a hybrid unsupervised clustering approach with manual class grouping and checking, followed by change detection post-classification comparison analysis. The results show that shrub encroachment is indeed occurring, a finding evidenced through three fine resolution replicate images plus medium resolution imagery. The results also demonstrate the complementarity of medium and fine resolution imagery, though some thematic information must be sacrificed to maintain high medium resolution classification accuracy. Finally, the findings have broader implications for issues such as vegetation seasonality, spatial transferability and management practices.

Discovering Cohorts of Pregnant Women From Social Media for Safety Surveillance and Analysis.

PubMed

Sarker, Abeed; Chandrashekar, Pramod; Magge, Arjun; Cai, Haitao; Klein, Ari; Gonzalez, Graciela

2017-10-30

Pregnancy exposure registries are the primary sources of information about the safety of maternal usage of medications during pregnancy. Such registries enroll pregnant women in a voluntary fashion early on in pregnancy and follow them until the end of pregnancy or longer to systematically collect information regarding specific pregnancy outcomes. Although the model of pregnancy registries has distinct advantages over other study designs, they are faced with numerous challenges and limitations such as low enrollment rate, high cost, and selection bias. The primary objectives of this study were to systematically assess whether social media (Twitter) can be used to discover cohorts of pregnant women and to develop and deploy a natural language processing and machine learning pipeline for the automatic collection of cohort information. In addition, we also attempted to ascertain, in a preliminary fashion, what types of longitudinal information may potentially be mined from the collected cohort information. Our discovery of pregnant women relies on detecting pregnancy-indicating tweets (PITs), which are statements posted by pregnant women regarding their pregnancies. We used a set of 14 patterns to first detect potential PITs. We manually annotated a sample of 14,156 of the retrieved user posts to distinguish real PITs from false positives and trained a supervised classification system to detect real PITs. We optimized the classification system via cross validation, with features and settings targeted toward optimizing precision for the positive class. For users identified to be posting real PITs via automatic classification, our pipeline collected all their available past and future posts from which other information (eg, medication usage and fetal outcomes) may be mined. Our rule-based PIT detection approach retrieved over 200,000 posts over a period of 18 months. Manual annotation agreement for three annotators was very high at kappa (κ)=.79. On a blind test set, the implemented classifier obtained an overall F 1 score of 0.84 (0.88 for the pregnancy class and 0.68 for the nonpregnancy class). Precision for the pregnancy class was 0.93, and recall was 0.84. Feature analysis showed that the combination of dense and sparse vectors for classification achieved optimal performance. Employing the trained classifier resulted in the identification of 71,954 users from the collected posts. Over 250 million posts were retrieved for these users, which provided a multitude of longitudinal information about them. Social media sources such as Twitter can be used to identify large cohorts of pregnant women and to gather longitudinal information via automated processing of their postings. Considering the many drawbacks and limitations of pregnancy registries, social media mining may provide beneficial complementary information. Although the cohort sizes identified over social media are large, future research will have to assess the completeness of the information available through them. ©Abeed Sarker, Pramod Chandrashekar, Arjun Magge, Haitao Cai, Ari Klein, Graciela Gonzalez. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 30.10.2017.

Intelligent detection of cracks in metallic surfaces using a waveguide sensor loaded with metamaterial elements.

PubMed

Ali, Abdulbaset; Hu, Bing; Ramahi, Omar

2015-05-15

This work presents a real life experiment of implementing an artificial intelligence model for detecting sub-millimeter cracks in metallic surfaces on a dataset obtained from a waveguide sensor loaded with metamaterial elements. Crack detection using microwave sensors is typically based on human observation of change in the sensor's signal (pattern) depicted on a high-resolution screen of the test equipment. However, as demonstrated in this work, implementing artificial intelligence to classify cracked from non-cracked surfaces has appreciable impact in terms of sensing sensitivity, cost, and automation. Furthermore, applying artificial intelligence for post-processing data collected from microwave sensors is a cornerstone for handheld test equipment that can outperform rack equipment with large screens and sophisticated plotting features. The proposed method was tested on a metallic plate with different cracks and the obtained experimental results showed good crack classification accuracy rates.

Intelligent Detection of Cracks in Metallic Surfaces Using a Waveguide Sensor Loaded with Metamaterial Elements

PubMed Central

Ali, Abdulbaset; Hu, Bing; Ramahi, Omar M.

2015-01-01

This work presents a real-life experiment implementing an artificial intelligence model for detecting sub-millimeter cracks in metallic surfaces on a dataset obtained from a waveguide sensor loaded with metamaterial elements. Crack detection using microwave sensors is typically based on human observation of change in the sensor's signal (pattern) depicted on a high-resolution screen of the test equipment. However, as demonstrated in this work, implementing artificial intelligence to classify cracked from non-cracked surfaces has appreciable impacts in terms of sensing sensitivity, cost, and automation. Furthermore, applying artificial intelligence for post-processing the data collected from microwave sensors is a cornerstone for handheld test equipment that can outperform rack equipment with large screens and sophisticated plotting features. The proposed method was tested on a metallic plate with different cracks, and the experimental results showed good crack classification accuracy rates. PMID:25988871

Topological Relations-Based Detection of Spatial Inconsistency in GLOBELAND30

NASA Astrophysics Data System (ADS)

Kang, S.; Chen, J.; Peng, S.

2017-09-01

Land cover is one of the fundamental data sets on environment assessment, land management and biodiversity protection, etc. Hence, data quality control of land cover is extremely critical for geospatial analysis and decision making. Due to the similar remote-sensing reflectance for some land cover types, omission and commission errors occurred in preliminary classification could result to spatial inconsistency between land cover types. In the progress of post-classification, this error checking mainly depends on manual labour to assure data quality, by which it is time-consuming and labour intensive. So a method required for automatic detection in post-classification is still an open issue. From logical inconsistency point of view, an inconsistency detection method is designed. This method consist of a grids extended 4-intersection model (GE4IM) for topological representation in single-valued space, by which three different kinds of topological relations including disjoint, touch, contain or contained-by are described, and an algorithm of region overlay for the computation of spatial inconsistency. The rules are derived from universal law in nature between water body and wetland, cultivated land and artificial surface. Through experiment conducted in Shandong Linqu County, data inconsistency can be pointed out within 6 minutes through calculation of topological inconsistency between cultivated land and artificial surface, water body and wetland. The efficiency evaluation of the presented algorithm is demonstrated by Google Earth images. Through comparative analysis, the algorithm is proved to be promising for inconsistency detection in land cover data.

Spatial and Temporal Patterns of Unburned Areas within Fire Perimeters in the Northwestern United States from 1984 to 2014

NASA Astrophysics Data System (ADS)

Meddens, A. J.; Kolden, C.; Lutz, J. A.; Abatzoglou, J. T.; Hudak, A. T.

2016-12-01

Recently, there has been concern about increasing extent and severity of wildfires across the globe given rapid climate change. Areas that do not burn within fire perimeters can act as fire refugia, providing (1) protection from the detrimental effects of the fire, (2) seed sources, and (3) post-fire habitat on the landscape. However, recent studies have mainly focused on the higher end of the burn severity spectrum whereas the lower end of the burn severity spectrum has been largely ignored. We developed a spatially explicit database for 2,200 fires across the inland northwestern USA, delineating unburned areas within fire perimeters from 1984 to 2014. We used 1,600 Landsat scenes with one or two scenes before and one or two scenes after the fires to capture the unburned proportion of the fire. Subsequently, we characterized the spatial and temporal patterns of unburned areas and related the unburned proportion to interannual climate variability. The overall classification accuracy detecting unburned locations was 89.2% using a 10-fold cross-validation classification tree approach in combination with 719 randomly located field plots. The unburned proportion ranged from 2% to 58% with an average of 19% for a select number of fires. We find that using both an immediate post-fire image and a one-year post fire image improves classification accuracy of unburned islands over using just a single post-fire image. The spatial characteristics of the unburned islands differ between forested and non-forested regions with a larger amount of unburned area within non-forest. In addition, we show trends of unburned proportion related primarily to concurrent climatic drought conditions across the entire region. This database is important for subsequent analyses of fire refugia prioritization, vegetation recovery studies, ecosystem resilience, and forest management to facilitate unburned islands through fuels breaks, prescribed burning, and fire suppression strategies.

Fast and Robust Segmentation and Classification for Change Detection in Urban Point Clouds

NASA Astrophysics Data System (ADS)

Roynard, X.; Deschaud, J.-E.; Goulette, F.

2016-06-01

Change detection is an important issue in city monitoring to analyse street furniture, road works, car parking, etc. For example, parking surveys are needed but are currently a laborious task involving sending operators in the streets to identify the changes in car locations. In this paper, we propose a method that performs a fast and robust segmentation and classification of urban point clouds, that can be used for change detection. We apply this method to detect the cars, as a particular object class, in order to perform parking surveys automatically. A recently proposed method already addresses the need for fast segmentation and classification of urban point clouds, using elevation images. The interest to work on images is that processing is much faster, proven and robust. However there may be a loss of information in complex 3D cases: for example when objects are one above the other, typically a car under a tree or a pedestrian under a balcony. In this paper we propose a method that retain the three-dimensional information while preserving fast computation times and improving segmentation and classification accuracy. It is based on fast region-growing using an octree, for the segmentation, and specific descriptors with Random-Forest for the classification. Experiments have been performed on large urban point clouds acquired by Mobile Laser Scanning. They show that the method is as fast as the state of the art, and that it gives more robust results in the complex 3D cases.

Oil detection in a coastal marsh with polarimetric Synthetic Aperture Radar (SAR)

USGS Publications Warehouse

Ramsey, Elijah W.; Rangoonwala, Amina; Suzuoki, Yukihiro; Jones, Cathleen E.

2011-01-01

The National Aeronautics and Space Administration's airborne Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) was deployed in June 2010 in response to the Deepwater Horizon oil spill in the Gulf of Mexico. UAVSAR is a fully polarimetric L-band Synthetic Aperture Radar (SAR) sensor for obtaining data at high spatial resolutions. Starting a month prior to the UAVSAR collections, visual observations confirmed oil impacts along shorelines within northeastern Barataria Bay waters in eastern coastal Louisiana. UAVSAR data along several flight lines over Barataria Bay were collected on 23 June 2010, including the repeat flight line for which data were collected in June 2009. Our analysis of calibrated single-look complex data for these flight lines shows that structural damage of shoreline marsh accompanied by oil occurrence manifested as anomalous features not evident in pre-spill data. Freeman-Durden (FD) and Cloude-Pottier (CP) decompositions of the polarimetric data and Wishart classifications seeded with the FD and CP classes also highlighted these nearshore features as a change in dominant scattering mechanism. All decompositions and classifications also identify a class of interior marshes that reproduce the spatially extensive changes in backscatter indicated by the pre- and post-spill comparison of multi-polarization radar backscatter data. FD and CP decompositions reveal that those changes indicate a transform of dominant scatter from primarily surface or volumetric to double or even bounce. Given supportive evidence that oil-polluted waters penetrated into the interior marshes, it is reasonable that these backscatter changes correspond with oil exposure; however, multiple factors prevent unambiguous determination of whether UAVSAR detected oil in interior marshes.

Mapping and improving frequency, accuracy, and interpretation of land cover change: Classifying coastal Louisiana with 1990, 1993, 1996, and 1999 Landsat Thematic Mapper image data

USGS Publications Warehouse

Nelson, G.; Ramsey, Elijah W.; Rangoonwala, A.

2005-01-01

Landsat Thematic Mapper images and collateral data sources were used to classify the land cover of the Mermentau River Basin within the chenier coastal plain and the adjacent uplands of Louisiana, USA. Landcover classes followed that of the National Oceanic and Atmospheric Administration's Coastal Change Analysis Program; however, classification methods needed to be developed to meet these national standards. Our first classification was limited to the Mermentau River Basin (MRB) in southcentral Louisiana, and the years of 1990, 1993, and 1996. To overcome problems due to class spectral inseparable, spatial and spectra continuums, mixed landcovers, and abnormal transitions, we separated the coastal area into regions of commonality and applying masks to specific land mixtures. Over the three years and 14 landcover classes (aggregating the cultivated land and grassland, and water and floating vegetation classes), overall accuracies ranged from 82% to 90%. To enhance landcover change interpretation, three indicators were introduced as Location Stability, Residence stability, and Turnover. Implementing methods substantiated in the multiple date MRB classification, we spatially extended the classification to the entire Louisiana coast and temporally extended the original 1990, 1993, 1996 classifications to 1999 (Figure 1). We also advanced the operational functionality of the classification and increased the credibility of change detection results. Increased operational functionality that resulted in diminished user input was for the most part gained by implementing a classification logic based on forbidden transitions. The logic detected and corrected misclassifications and mostly alleviated the necessity of subregion separation prior to the classification. The new methods provided an improved ability for more timely detection and response to landcover impact. ?? 2005 IEEE.

Building Change Detection from Bi-Temporal Dense-Matching Point Clouds and Aerial Images.

PubMed

Pang, Shiyan; Hu, Xiangyun; Cai, Zhongliang; Gong, Jinqi; Zhang, Mi

2018-03-24

In this work, a novel building change detection method from bi-temporal dense-matching point clouds and aerial images is proposed to address two major problems, namely, the robust acquisition of the changed objects above ground and the automatic classification of changed objects into buildings or non-buildings. For the acquisition of changed objects above ground, the change detection problem is converted into a binary classification, in which the changed area above ground is regarded as the foreground and the other area as the background. For the gridded points of each period, the graph cuts algorithm is adopted to classify the points into foreground and background, followed by the region-growing algorithm to form candidate changed building objects. A novel structural feature that was extracted from aerial images is constructed to classify the candidate changed building objects into buildings and non-buildings. The changed building objects are further classified as "newly built", "taller", "demolished", and "lower" by combining the classification and the digital surface models of two periods. Finally, three typical areas from a large dataset are used to validate the proposed method. Numerous experiments demonstrate the effectiveness of the proposed algorithm.

Responsiveness, Sensitivity, and Minimally Detectable Difference of the Graded and Redefined Assessment of Strength, Sensibility, and Prehension, Version 1.0.

PubMed

Kalsi-Ryan, Sukhvinder; Beaton, Dorcas; Ahn, Henry; Askes, Heather; Drew, Brian; Curt, Armin; Popovic, Milos R; Wang, Justin; Verrier, Mary C; Fehlings, Michael G

2016-02-01

As spinal cord injury (SCI) trials begin to involve subjects with acute cervical SCI, establishing the property of an upper limb outcome measure to detect change over time is critical for its usefulness in clinical trials. The objectives of this study were to define responsiveness, sensitivity, and minimally detectable difference (MDD) of the Graded Redefined Assessment of Strength, Sensibility, and Prehension (GRASSP). An observational, longitudinal study was conducted. International Standards of Neurological Classification of SCI (ISNCSCI), GRASSP, Capabilities of Upper Extremity Questionnaire (CUE-Q), and Spinal Cord Independence Measure (SCIM) were administered 0-10 days, 1, 3, 6, and 12 months post-injury. Standardized Response Means (SRM) for GRASSP and ISNCSCI measures were calculated. Longitudinal construct validity was calculated using Pearson correlation coefficients. Smallest real difference for all subtests was calculated to define the MDD values for all GRASSP subtests. Longitudinal construct validity demonstrated GRASSP and all external measures to be responsive to neurological change for 1 year post-injury. SRM values for the GRASSP subtests ranged from 0.25 to 0.85 units greater than that for ISNCSCI strength and sensation, SCIM-SS, and CUE-Q. MDD values for GRASSP subtests ranged from 2-5 points. GRASSP demonstrates good responsiveness and excellent sensitivity that is superior to ISNCSCI and SCIM III. MDD values are useful in the evaluation of interventions in both clinical and research settings. The responsiveness and sensitivity of GRASSP make it a valuable condition-specific measure in tetraplegia, where changes in upper limb neurological and functional outcomes are essential for evaluating the efficacy of interventions.

Region-Based Building Rooftop Extraction and Change Detection

NASA Astrophysics Data System (ADS)

Tian, J.; Metzlaff, L.; d'Angelo, P.; Reinartz, P.

2017-09-01

Automatic extraction of building changes is important for many applications like disaster monitoring and city planning. Although a lot of research work is available based on 2D as well as 3D data, an improvement in accuracy and efficiency is still needed. The introducing of digital surface models (DSMs) to building change detection has strongly improved the resulting accuracy. In this paper, a post-classification approach is proposed for building change detection using satellite stereo imagery. Firstly, DSMs are generated from satellite stereo imagery and further refined by using a segmentation result obtained from the Sobel gradients of the panchromatic image. Besides the refined DSMs, the panchromatic image and the pansharpened multispectral image are used as input features for mean-shift segmentation. The DSM is used to calculate the nDSM, out of which the initial building candidate regions are extracted. The candidate mask is further refined by morphological filtering and by excluding shadow regions. Following this, all segments that overlap with a building candidate region are determined. A building oriented segments merging procedure is introduced to generate a final building rooftop mask. As the last step, object based change detection is performed by directly comparing the building rooftops extracted from the pre- and after-event imagery and by fusing the change indicators with the roof-top region map. A quantitative and qualitative assessment of the proposed approach is provided by using WorldView-2 satellite data from Istanbul, Turkey.

An Investigation of Automatic Change Detection for Topographic Map Updating

NASA Astrophysics Data System (ADS)

Duncan, P.; Smit, J.

2012-08-01

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

Urban Change Detection of Pingtan City based on Bi-temporal Remote Sensing Images

NASA Astrophysics Data System (ADS)

Degang, JIANG; Jinyan, XU; Yikang, GAO

2017-02-01

In this paper, a pair of SPOT 5-6 images with the resolution of 0.5m is selected. An object-oriented classification method is used to the two images and five classes of ground features were identified as man-made objects, farmland, forest, waterbody and unutilized land. An auxiliary ASTER GDEM was used to improve the classification accuracy. And the change detection based on the classification results was performed. Accuracy assessment was carried out finally. Consequently, satisfactory results were obtained. The results show that great changes of the Pingtan city have been detected as the expansion of the city area and the intensity increase of man-made buildings, roads and other infrastructures with the establishment of Pingtan comprehensive experimental zone. Wide range of open sea area along the island coast zones has been reclaimed for port and CBDs construction.

Validation of a new classification for periprosthetic shoulder fractures.

PubMed

Kirchhoff, Chlodwig; Beirer, Marc; Brunner, Ulrich; Buchholz, Arne; Biberthaler, Peter; Crönlein, Moritz

2018-06-01

Successful treatment of periprosthetic shoulder fractures depends on the right strategy, starting with a well-structured classification of the fracture. Unfortunately, clinically relevant factors for treatment planning are missing in the pre-existing classifications. Therefore, the aim of the present study was to describe a new specific classification system for periprosthetic shoulder fractures including a structured treatment algorithm for this important fragility fracture issue. The classification was established, focussing on five relevant items, naming the prosthesis type, the fracture localisation, the rotator cuff status, the anatomical fracture region and the stability of the implant. After considering each single item, the individual treatment concept can be assessed in one last step. To evaluate the introduced classification, a retrospective analysis of pre- and post-operative data of patients, treated with periprosthetic shoulder fractures, was conducted by two board certified trauma surgery consultants. The data of 19 patients (8 male, 11 female) with a mean age of 74 ± five years have been analysed in our study. The suggested treatment algorithm was proven to be reliable, detected by good clinical outcome in 15 of 16 (94%) cases, where the suggested treatment was maintained. Only one case resulted in poor outcome due to post-operative wound infection and had to be revised. The newly developed six-step classification is easy to utilise and extends the pre-existing classification systems in terms of clinically-relevant information. This classification should serve as a simple tool for the surgeon to consider the optimal treatment for his patients.

Change detection and classification in brain MR images using change vector analysis.

PubMed

Simões, Rita; Slump, Cornelis

2011-01-01

The automatic detection of longitudinal changes in brain images is valuable in the assessment of disease evolution and treatment efficacy. Most existing change detection methods that are currently used in clinical research to monitor patients suffering from neurodegenerative diseases--such as Alzheimer's--focus on large-scale brain deformations. However, such patients often have other brain impairments, such as infarcts, white matter lesions and hemorrhages, which are typically overlooked by the deformation-based methods. Other unsupervised change detection algorithms have been proposed to detect tissue intensity changes. The outcome of these methods is typically a binary change map, which identifies changed brain regions. However, understanding what types of changes these regions underwent is likely to provide equally important information about lesion evolution. In this paper, we present an unsupervised 3D change detection method based on Change Vector Analysis. We compute and automatically threshold the Generalized Likelihood Ratio map to obtain a binary change map. Subsequently, we perform histogram-based clustering to classify the change vectors. We obtain a Kappa Index of 0.82 using various types of simulated lesions. The classification error is 2%. Finally, we are able to detect and discriminate both small changes and ventricle expansions in datasets from Mild Cognitive Impairment patients.

Classification of ictal and seizure-free HRV signals with focus on lateralization of epilepsy.

PubMed

Behbahani, Soroor; Dabanloo, Nader Jafarnia; Nasrabadi, Ali Motie; Dourado, Antonio

2016-01-01

Epileptic onsets often affect the autonomic function of the body during a seizure, whether it is in ictal, interictal or post-ictal periods. The different effects of localization and lateralization of seizures on heart rate variability (HRV) emphasize the importance of autonomic function changes in epileptic patients. On the other hand, the detection of seizures is of primary interests in evaluating the epileptic patients. In the current paper, we analyzed the HRV signal to develop a reliable offline seizure-detection algorithm to focus on the effects of lateralization on HRV. We assessed the HRV during 5-min segments of continuous electrocardiogram (ECG) recording with a total number of 170 seizures occurred in 16 patients, composed of 86 left-sided and 84 right-sided focus seizures. Relatively high and low-frequency components of the HRV were computed using spectral analysis. Poincaré parameters of each heart rate time series considered as non-linear features. We fed these features to the Support Vector Machines (SVMs) to find a robust classification method to classify epileptic and non-epileptic signals. Leave One Out Cross-Validation (LOOCV) approach was used to demonstrate the consistency of the classification results. Our obtained classification accuracy confirms that the proposed scheme has a potential in classifying HRV signals to epileptic and non-epileptic classes. The accuracy rates for right-sided and left-sided focus seizures were obtained as 86.74% and 79.41%, respectively. The main finding of our study is that the patients with right-sided focus epilepsy showed more reduction in parasympathetic activity and more increase in sympathetic activity. It can be a marker of impaired vagal activity associated with increased cardiovascular risk and arrhythmias. Our results suggest that lateralization of the seizure onset zone could exert different influences on heart rate changes. A right-sided seizure would cause an ictal tachycardia whereas a left-sided seizure would result in an ictal bradycardia.

Contribution of National near Real Time MODIS Forest Maximum Percentage NDVI Change Products to the U.S. ForWarn System

NASA Technical Reports Server (NTRS)

Spruce, Joseph P.; Hargrove, William; Gasser, Gerald; Smoot, James; Kuper, Philip D.

2012-01-01

This presentation reviews the development, integration, and testing of Near Real Time (NRT) MODIS forest % maximum NDVI change products resident to the USDA Forest Service (USFS) ForWarn System. ForWarn is an Early Warning System (EWS) tool for detection and tracking of regionally evident forest change, which includes the U.S. Forest Change Assessment Viewer (FCAV) (a publically available on-line geospatial data viewer for visualizing and assessing the context of this apparent forest change). NASA Stennis Space Center (SSC) is working collaboratively with the USFS, ORNL, and USGS to contribute MODIS forest change products to ForWarn. These change products compare current NDVI derived from expedited eMODIS data, to historical NDVI products derived from MODIS MOD13 data. A new suite of forest change products are computed every 8 days and posted to the ForWarn system; this includes three different forest change products computed using three different historical baselines: 1) previous year; 2) previous three years; and 3) all previous years in the MODIS record going back to 2000. The change product inputs are maximum value NDVI that are composited across a 24 day interval and refreshed every 8 days so that resulting images for the conterminous U.S. are predominantly cloud-free yet still retain temporally relevant fresh information on changes in forest canopy greenness. These forest change products are computed at the native nominal resolution of the input reflectance bands at 231.66 meters, which equates to approx 5.4 hectares or 13.3 acres per pixel. The Time Series Product Tool, a MATLAB-based software package developed at NASA SSC, is used to temporally process, fuse, reduce noise, interpolate data voids, and re-aggregate the historical NDVI into 24 day composites, and then custom MATLAB scripts are used to temporally process the eMODIS NDVIs so that they are in synch with the historical NDVI products. Prior to posting, an in-house snow mask classification product is computed for the current compositing period and integrated into the change images to account for snow related NDVI drops. The supplemental snow classification product was needed because other available QA cloud/snow mask typically underestimates snow cover. MODIS true and false color composites were also computed from eMODIS reflectance data and the true color RGBs are also posted on ForWarn?s FCAV; this data is used for assessing apparent occasional quality issues on the change products due to residual unmasked cloud cover. New forest change products are posted with typical latencies of 1-2 days after the last input eMODIS data collection date for a given 24 day compositing period.

Classification of hydrocephalus: critical analysis of classification categories and advantages of "Multi-categorical Hydrocephalus Classification" (Mc HC).

PubMed

Oi, Shizuo

2011-10-01

Hydrocephalus is a complex pathophysiology with disturbed cerebrospinal fluid (CSF) circulation. There are numerous numbers of classification trials published focusing on various criteria, such as associated anomalies/underlying lesions, CSF circulation/intracranial pressure patterns, clinical features, and other categories. However, no definitive classification exists comprehensively to cover the variety of these aspects. The new classification of hydrocephalus, "Multi-categorical Hydrocephalus Classification" (Mc HC), was invented and developed to cover the entire aspects of hydrocephalus with all considerable classification items and categories. Ten categories include "Mc HC" category I: onset (age, phase), II: cause, III: underlying lesion, IV: symptomatology, V: pathophysiology 1-CSF circulation, VI: pathophysiology 2-ICP dynamics, VII: chronology, VII: post-shunt, VIII: post-endoscopic third ventriculostomy, and X: others. From a 100-year search of publication related to the classification of hydrocephalus, 14 representative publications were reviewed and divided into the 10 categories. The Baumkuchen classification graph made from the round o'clock classification demonstrated the historical tendency of deviation to the categories in pathophysiology, either CSF or ICP dynamics. In the preliminary clinical application, it was concluded that "Mc HC" is extremely effective in expressing the individual state with various categories in the past and present condition or among the compatible cases of hydrocephalus along with the possible chronological change in the future.

Acoustic/Seismic Ground Sensors for Detection, Localization and Classification on the Battlefield

DTIC Science & Technology

2006-10-01

controlled so that collisions are avoided. Figure 1 presents BACH system components. 3 BACH Sensor Posts (1 to 8) Command Post BACH MMI PC VHF...2.2.4 Processing scheme Processing inside SP is dedicated to stationary spectral lines extraction and derives from ASW algorithms. Special attention...is similar to that used for helicopters (see figure 4), with adaptations to cope with vehicles signatures (fuzzy unstable spectral lines, abrupt

Forest land cover change (1975-2000) in the Greater Border Lakes region

Treesearch

Peter T. Wolter; Brian R. Sturtevant; Brian R. Miranda; Sue M. Lietz; Phillip A. Townsend; John Pastor

2012-01-01

This document and accompanying maps describe land cover classifications and change detection for a 13.8 million ha landscape straddling the border between Minnesota, and Ontario, Canada (greater Border Lakes Region). Land cover classifications focus on discerning Anderson Level II forest and nonforest cover to track spatiotemporal changes in forest cover. Multi-...

Flood Mapping in the Lower Mekong River Basin Using Daily MODIS Observations

NASA Technical Reports Server (NTRS)

Fayne, Jessica V.; Bolten, John D.; Doyle, Colin S.; Fuhrmann, Sven; Rice, Matthew T.; Houser, Paul R.; Lakshmi, Venkat

2017-01-01

In flat homogenous terrain such as in Cambodia and Vietnam, the monsoon season brings significant and consistent flooding between May and November. To monitor flooding in the Lower Mekong region, the near real-time NASA Flood Extent Product (NASA-FEP) was developed using seasonal normalized difference vegetation index (NDVI) differences from the 250 m resolution Moderate Resolution Imaging Spectroradiometer (MODIS) sensor compared to daily observations. The use of a percentage change interval classification relating to various stages of flooding reduces might be confusing to viewers or potential users, and therefore reducing the product usage. To increase the product usability through simplification, the classification intervals were compared with other commonly used change detection schemes to identify the change classification scheme that best delineates flooded areas. The percentage change method used in the NASA-FEP proved to be helpful in delineating flood boundaries compared to other change detection methods. The results of the accuracy assessments indicate that the -75% NDVI change interval can be reclassified to a descriptive 'flood' classification. A binary system was used to simplify the interpretation of the NASA-FEP by removing extraneous information from lower interval change classes.

"In my opinion, work would be in first place and family in second": young women's imagined gender-work relations in post-Soviet Lithuania.

PubMed

Reiter, Herwig

2010-01-01

This article explores young women's orientation to work and motherhood in the post-communist context of radical socio-economic transformation in Europe. Based on a qualitative-explorative study into meanings of work and unemployment among young people in post-Soviet Lithuania, the paper introduces an empirically grounded classification of imagined gender-work arrangements. The single patterns of the classification are based on the three configurations of work and motherhood, work and partnership, and work and provision. The findings inform the reconstruction of the 'landscape' of imagined gendered adulthoods in Europe as well as the analysis of emerging gender relations under conditions of rapid social change.

Land Cover Analysis by Using Pixel-Based and Object-Based Image Classification Method in Bogor

NASA Astrophysics Data System (ADS)

Amalisana, Birohmatin; Rokhmatullah; Hernina, Revi

2017-12-01

The advantage of image classification is to provide earth’s surface information like landcover and time-series changes. Nowadays, pixel-based image classification technique is commonly performed with variety of algorithm such as minimum distance, parallelepiped, maximum likelihood, mahalanobis distance. On the other hand, landcover classification can also be acquired by using object-based image classification technique. In addition, object-based classification uses image segmentation from parameter such as scale, form, colour, smoothness and compactness. This research is aimed to compare the result of landcover classification and its change detection between parallelepiped pixel-based and object-based classification method. Location of this research is Bogor with 20 years range of observation from 1996 until 2016. This region is famous as urban areas which continuously change due to its rapid development, so that time-series landcover information of this region will be interesting.

Detection and assessment of land use dynamics on Tenerife (Canary Islands): the agricultural development between 1986 and 2010

NASA Astrophysics Data System (ADS)

Günthert, Sebastian; Naumann, Simone; Siegmund, Alexander

2012-10-01

Since Spanish colonial times, the Canary Islands and especially Tenerife have always been used for intensive agriculture. Today almost 1/4 of the total area of Tenerife are agriculturally affected, whereas especially mountainous areas with suitable climate conditions are drastically transformed for agricultural use by building of large terraces. In recent years, political and economical developments lead to a further transformation process, especially inducted by an expansive tourism, which caused concentration- and intensification-tendencies of agricultural land use in lower altitudes as well as agricultural set-aside and rural exodus in the hinterland. The overall aim of the research at hand is to address the agricultural land use dynamics of the past decades, to statistically assess the causal reasons for those changes and to model the future agricultural land use dynamics on Tenerife. Therefore, an object-based classification procedure for recent RapidEye data (2010), Spot 4 (1998) as well as SPOT 1 (1986-88) imagery was developed, followed by a post classification comparison (PCC). Older agricultural fallow land or agricultural set-aside with a higher level of natural succession can hardly be acquired in the used medium satellite imagery. Hence, a second detection technique was generated, which allows an exact identification of the total agriculturally affected area on Tenerife, also containing older agricultural fallow land or agricultural set-aside. The method consists of an automatic texture-oriented detection and area-wide extraction of linear agricultural structures (plough furrows and field boundaries of arable land, utilised and non-utilised agricultural terraces) in current orthophotos of Tenerife. Once the change detection analysis is realised, it is necessary to identify the different driving forces which are responsible for the agricultural land use dynamics. The statistical connections between agricultural land use changes and these driving forces are identified by the use of correlation and regression analyses.

A Novel Segment-Based Approach for Improving Classification Performance of Transport Mode Detection.

PubMed

Guvensan, M Amac; Dusun, Burak; Can, Baris; Turkmen, H Irem

2017-12-30

Transportation planning and solutions have an enormous impact on city life. To minimize the transport duration, urban planners should understand and elaborate the mobility of a city. Thus, researchers look toward monitoring people's daily activities including transportation types and duration by taking advantage of individual's smartphones. This paper introduces a novel segment-based transport mode detection architecture in order to improve the results of traditional classification algorithms in the literature. The proposed post-processing algorithm, namely the Healing algorithm, aims to correct the misclassification results of machine learning-based solutions. Our real-life test results show that the Healing algorithm could achieve up to 40% improvement of the classification results. As a result, the implemented mobile application could predict eight classes including stationary, walking, car, bus, tram, train, metro and ferry with a success rate of 95% thanks to the proposed multi-tier architecture and Healing algorithm.

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.

Addressing multi-label imbalance problem of surgical tool detection using CNN.

PubMed

Sahu, Manish; Mukhopadhyay, Anirban; Szengel, Angelika; Zachow, Stefan

2017-06-01

A fully automated surgical tool detection framework is proposed for endoscopic video streams. State-of-the-art surgical tool detection methods rely on supervised one-vs-all or multi-class classification techniques, completely ignoring the co-occurrence relationship of the tools and the associated class imbalance. In this paper, we formulate tool detection as a multi-label classification task where tool co-occurrences are treated as separate classes. In addition, imbalance on tool co-occurrences is analyzed and stratification techniques are employed to address the imbalance during convolutional neural network (CNN) training. Moreover, temporal smoothing is introduced as an online post-processing step to enhance runtime prediction. Quantitative analysis is performed on the M2CAI16 tool detection dataset to highlight the importance of stratification, temporal smoothing and the overall framework for tool detection. The analysis on tool imbalance, backed by the empirical results, indicates the need and superiority of the proposed framework over state-of-the-art techniques.

Mixing geometric and radiometric features for change classification

NASA Astrophysics Data System (ADS)

Fournier, Alexandre; Descombes, Xavier; Zerubia, Josiane

2008-02-01

Most basic change detection algorithms use a pixel-based approach. Whereas such approach is quite well defined for monitoring important area changes (such as urban growth monitoring) in low resolution images, an object based approach seems more relevant when the change detection is specifically aimed toward targets (such as small buildings and vehicles). In this paper, we present an approach that mixes radiometric and geometric features to qualify the changed zones. The goal is to establish bounds (appearance, disappearance, substitution ...) between the detected changes and the underlying objects. We proceed by first clustering the change map (containing each pixel bitemporal radiosity) in different classes using the entropy-kmeans algorithm. Assuming that most man-made objects have a polygonal shape, a polygonal approximation algorithm is then used in order to characterize the resulting zone shapes. Hence allowing us to refine the primary rough classification, by integrating the polygon orientations in the state space. Tests are currently conducted on Quickbird data.

Cerebral perfusion alterations in epileptic patients during peri-ictal and post-ictal phase: PASL vs DSC-MRI.

PubMed

Pizzini, Francesca B; Farace, Paolo; Manganotti, Paolo; Zoccatelli, Giada; Bongiovanni, Luigi G; Golay, Xavier; Beltramello, Alberto; Osculati, Antonio; Bertini, Giuseppe; Fabene, Paolo F

2013-07-01

Non-invasive pulsed arterial spin labeling (PASL) MRI is a method to study brain perfusion that does not require the administration of a contrast agent, which makes it a valuable diagnostic tool as it reduces cost and side effects. The purpose of the present study was to establish the viability of PASL as an alternative to dynamic susceptibility contrast (DSC-MRI) and other perfusion imaging methods in characterizing changes in perfusion patterns caused by seizures in epileptic patients. We evaluated 19 patients with PASL. Of these, the 9 affected by high-frequency seizures were observed during the peri-ictal period (within 5hours since the last seizure), while the 10 patients affected by low-frequency seizures were observed in the post-ictal period. For comparison, 17/19 patients were also evaluated with DSC-MRI and CBF/CBV. PASL imaging showed focal vascular changes, which allowed the classification of patients in three categories: 8 patients characterized by increased perfusion, 4 patients with normal perfusion and 7 patients with decreased perfusion. PASL perfusion imaging findings were comparable to those obtained by DSC-MRI. Since PASL is a) sensitive to vascular alterations induced by epileptic seizures, b) comparable to DSC-MRI for detecting perfusion asymmetries, c) potentially capable of detecting time-related perfusion changes, it can be recommended for repeated evaluations, to identify the epileptic focus, and in follow-up and/or therapy-response assessment. Copyright © 2013 Elsevier Inc. All rights reserved.

Remote sensing and spatial analysis based study for detecting deforestation and the associated drivers

NASA Astrophysics Data System (ADS)

El-Abbas, Mustafa M.; Csaplovics, Elmar; Deafalla, Taisser H.

2013-10-01

Nowadays, remote-sensing technologies are becoming increasingly interlinked to the issue of deforestation. They offer a systematized and objective strategy to document, understand and simulate the deforestation process and its associated causes. In this context, the main goal of this study, conducted in the Blue Nile region of Sudan, in which most of the natural habitats were dramatically destroyed, was to develop spatial methodologies to assess the deforestation dynamics and its associated factors. To achieve that, optical multispectral satellite scenes (i.e., ASTER and LANDSAT) integrated with field survey in addition to multiple data sources were used for the analyses. Spatiotemporal Object Based Image Analysis (STOBIA) was applied to assess the change dynamics within the period of study. Broadly, the above mentioned analyses include; Object Based (OB) classifications, post-classification change detection, data fusion, information extraction and spatial analysis. Hierarchical multi-scale segmentation thresholds were applied and each class was delimited with semantic meanings by a set of rules associated with membership functions. Consequently, the fused multi-temporal data were introduced to create detailed objects of change classes from the input LU/LC classes. The dynamic changes were quantified and spatially located as well as the spatial and contextual relations from adjacent areas were analyzed. The main finding of the present study is that, the forest areas were drastically decreased, while the agrarian structure in conversion of forest into agricultural fields and grassland was the main force of deforestation. In contrast, the capability of the area to recover was clearly observed. The study concludes with a brief assessment of an 'oriented' framework, focused on the alarming areas where serious dynamics are located and where urgent plans and interventions are most critical, guided with potential solutions based on the identified driving forces.

Integrated Change Detection and Classification in Urban Areas Based on Airborne Laser Scanning Point Clouds.

PubMed

Tran, Thi Huong Giang; Ressl, Camillo; Pfeifer, Norbert

2018-02-03

This paper suggests a new approach for change detection (CD) in 3D point clouds. It combines classification and CD in one step using machine learning. The point cloud data of both epochs are merged for computing features of four types: features describing the point distribution, a feature relating to relative terrain elevation, features specific for the multi-target capability of laser scanning, and features combining the point clouds of both epochs to identify the change. All these features are merged in the points and then training samples are acquired to create the model for supervised classification, which is then applied to the whole study area. The final results reach an overall accuracy of over 90% for both epochs of eight classes: lost tree, new tree, lost building, new building, changed ground, unchanged building, unchanged tree, and unchanged ground.

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Urban forest topographical mapping using UAV LIDAR

NASA Astrophysics Data System (ADS)

Putut Ash Shidiq, Iqbal; Wibowo, Adi; Kusratmoko, Eko; Indratmoko, Satria; Ardhianto, Ronni; Prasetyo Nugroho, Budi

2017-12-01

Topographical data is highly needed by many parties, such as government institution, mining companies and agricultural sectors. It is not just about the precision, the acquisition time and data processing are also carefully considered. In relation with forest management, a high accuracy topographic map is necessary for planning, close monitoring and evaluating forest changes. One of the solution to quickly and precisely mapped topography is using remote sensing system. In this study, we test high-resolution data using Light Detection and Ranging (LiDAR) collected from unmanned aerial vehicles (UAV) to map topography and differentiate vegetation classes based on height in urban forest area of University of Indonesia (UI). The semi-automatic and manual classifications were applied to divide point clouds into two main classes, namely ground and vegetation. There were 15,806,380 point clouds obtained during the post-process, in which 2.39% of it were detected as ground.

Oral carcinoma after hematopoietic stem cell transplantation – a new classification based on a literature review over 30 years

PubMed Central

Kruse, Astrid LD; Grätz, Klaus W

2009-01-01

Background Patients undergoing hematopoietic stem cell transplantation (HSCT) have a higher risk of developing secondary solid tumors, in particular squamous cell carcinoma, because of several risk factors, including full-body irradiation (TBI), chemotherapy, and chronic graft versus host disease (GVHD). Based on the review presented here, a classification of oral changes is suggested in order to provide a tool to detect high-risk patients. Methods and Results The literature over the last 30 years was reviewed for development of malignoma of the oral cavity after HSCT. Overall, 64 cases were found. In 16 out of 30 cases, the tongue was the primary location, followed by the salivary gland (10 out of 30); 56.4% appeared in a latency time of 5 to 9 years after HSCT. In 76.6%, GVHD was noticed before the occurrence of oral malignancy. Premalignant changes of the oral mucosa were mucositis, xerostomia, and lichenoid changes, developing into erosive form. Conclusion All physicians involved in the treatment of post-HSCT patients should be aware of the increased risk, even after 5 years from the development of oral malignancy, in particular when oral graft versus host changes are visible. In order to develop evidence based management, screening and offer adequate therapy as early as possible in this patient group, multicenter studies, involving oncologists and head and neck surgeons, should be established. PMID:19624855

Changes in Crohn's disease phenotype over time in the Chinese population: validation of the Montreal classification system.

PubMed

Chow, Dorothy K L; Leong, Rupert W L; Lai, Larry H; Wong, Grace L H; Leung, Wai-Keung; Chan, Francis K L; Sung, Joseph J Y

2008-04-01

Phenotypic evolution of Crohn's disease occurs in whites but has never been described in other populations. The Montreal classification may describe phenotypes more precisely. The aim of this study was to validate the Montreal classification through a longitudinal sensitivity analysis in detecting phenotypic variation compared to the Vienna classification. This was a retrospective longitudinal study of consecutive Chinese Crohn's disease patients. All cases were classified by the Montreal classification and the Vienna classification for behavior and location. The evolution of these characteristics and the need for surgery were evaluated. A total of 109 patients were recruited (median follow-up: 4 years, range: 6 months-18 years). Crohn's disease behavior changed 3 years after diagnosis (P = 0.025), with an increase in stricturing and penetrating phenotypes, as determined by the Montreal classification, but was only detected by the Vienna classification after 5 years (P = 0.015). Disease location remained stable on follow-up in both classifications. Thirty-four patients (31%) underwent major surgery during the follow-up period with the stricturing [P = 0.002; hazard ratio (HR): 3.3; 95% CI: 1.5-7.0] and penetrating (P = 0.03; HR: 5.8; 95% CI: 1.2-28.2) phenotypes according to the Montreal classification associated with the need for major surgery. In contrast, colonic disease was protective against a major operation (P = 0.02; HR: 0.3; 95% CI: 0.08-0.8). This is the first study demonstrating phenotypic evolution of Crohn's disease in a nonwhite population. The Montreal classification is more sensitive to behavior phenotypic changes than is the Vienna classification after excluding perianal disease from the penetrating disease category and was useful in predicting course and the need for surgery.

Object-based analysis of multispectral airborne laser scanner data for land cover classification and map updating

NASA Astrophysics Data System (ADS)

Matikainen, Leena; Karila, Kirsi; Hyyppä, Juha; Litkey, Paula; Puttonen, Eetu; Ahokas, Eero

2017-06-01

During the last 20 years, airborne laser scanning (ALS), often combined with passive multispectral information from aerial images, has shown its high feasibility for automated mapping processes. The main benefits have been achieved in the mapping of elevated objects such as buildings and trees. Recently, the first multispectral airborne laser scanners have been launched, and active multispectral information is for the first time available for 3D ALS point clouds from a single sensor. This article discusses the potential of this new technology in map updating, especially in automated object-based land cover classification and change detection in a suburban area. For our study, Optech Titan multispectral ALS data over a suburban area in Finland were acquired. Results from an object-based random forests analysis suggest that the multispectral ALS data are very useful for land cover classification, considering both elevated classes and ground-level classes. The overall accuracy of the land cover classification results with six classes was 96% compared with validation points. The classes under study included building, tree, asphalt, gravel, rocky area and low vegetation. Compared to classification of single-channel data, the main improvements were achieved for ground-level classes. According to feature importance analyses, multispectral intensity features based on several channels were more useful than those based on one channel. Automatic change detection for buildings and roads was also demonstrated by utilising the new multispectral ALS data in combination with old map vectors. In change detection of buildings, an old digital surface model (DSM) based on single-channel ALS data was also used. Overall, our analyses suggest that the new data have high potential for further increasing the automation level in mapping. Unlike passive aerial imaging commonly used in mapping, the multispectral ALS technology is independent of external illumination conditions, and there are no shadows on intensity images produced from the data. These are significant advantages in developing automated classification and change detection procedures.

PCA feature extraction for change detection in multidimensional unlabeled data.

PubMed

Kuncheva, Ludmila I; Faithfull, William J

2014-01-01

When classifiers are deployed in real-world applications, it is assumed that the distribution of the incoming data matches the distribution of the data used to train the classifier. This assumption is often incorrect, which necessitates some form of change detection or adaptive classification. While there has been a lot of work on change detection based on the classification error monitored over the course of the operation of the classifier, finding changes in multidimensional unlabeled data is still a challenge. Here, we propose to apply principal component analysis (PCA) for feature extraction prior to the change detection. Supported by a theoretical example, we argue that the components with the lowest variance should be retained as the extracted features because they are more likely to be affected by a change. We chose a recently proposed semiparametric log-likelihood change detection criterion that is sensitive to changes in both mean and variance of the multidimensional distribution. An experiment with 35 datasets and an illustration with a simple video segmentation demonstrate the advantage of using extracted features compared to raw data. Further analysis shows that feature extraction through PCA is beneficial, specifically for data with multiple balanced classes.

Quantitative OCT and MRI biomarkers for the differentiation of cartilage degeneration.

PubMed

Nebelung, Sven; Brill, Nicolai; Tingart, Markus; Pufe, Thomas; Kuhl, Christiane; Jahr, Holger; Truhn, Daniel

2016-04-01

To evaluate the usefulness of quantitative parameters obtained by optical coherence tomography (OCT) and magnetic resonance imaging (MRI) in the comprehensive assessment of human articular cartilage degeneration. Human osteochondral samples of variable degeneration (n = 45) were obtained from total knee replacements and assessed by MRI sequences measuring T1, T1ρ, T2 and T2* relaxivity and by OCT-based quantification of irregularity (OII, optical irregularity index), homogeneity (OHI, optical homogeneity index]) and attenuation (OAI, optical attenuation index]). Samples were also assessed macroscopically (Outerbridge classification) and histologically (Mankin classification) as grade-0 (Mankin scores 0-4)/grade-I (scores 5-8)/grade-II (scores 9-10)/grade-III (score 11-14). After data normalisation, differences between Mankin grades and correlations between imaging parameters were assessed using ANOVA and Tukey's post-hoc test and Spearman's correlation coefficients, respectively. Sensitivities and specificities in the detection of Mankin grade-0 were calculated. Significant degeneration-related increases were found for T2 and OII and decreases for OAI, while T1, T1ρ, T2* or OHI did not reveal significant changes in relation to degeneration. A number of significant correlations between imaging parameters and histological (sub)scores were found, in particular for T2 and OII. Sensitivities and specificities in the detection of Mankin grade-0 were highest for OHI/T1 and OII/T1ρ, respectively. Quantitative OCT and MRI techniques seem to complement each other in the comprehensive assessment of cartilage degeneration. Sufficiently large structural and compositional changes in the extracellular matrix may thus be parameterized and quantified, while the detection of early degeneration remains challenging.

Measuring patterns of disability using the International Classification of Functioning, Disability and Health in the post-acute stroke rehabilitation setting.

PubMed

Goljar, Nika; Burger, Helena; Vidmar, Gaj; Leonardi, Matilde; Marincek, Crt

2011-06-01

To determine whether the International Classification of Functioning, Disability and Health (ICF) model is adequate for assessing disability patterns in stroke survivors in the sub-acute rehabilitation setting in terms of potential changes in functional profiles over time. Functional profiles of 197 stroke patients were assessed using the ICF Checklist and the Functional Independence Measure (FIMTM) at admission and discharge from rehabilitation hospital. The ICF Checklist was applied based on medical documentation and rehabilitation team meetings. Descriptive analyses were performed to identify changes in ICF categories and qualifiers from admission to discharge, and correlations between different improvement measures were calculated. Mean rehabilitation duration was 60 days; patients' mean age was 60 years, with mean FIM-score 75 at admission. Mean FIM-score improvement at discharge was 12.5. Within Body Functions, changes in at least 10% of patients were found regarding 13 categories; no categories within Body Structures, 24 within Activities and Participation, and 2 within Environmental Factors. Changes were mostly due to improvement in qualifiers, except for within Environmental Factors, where they were due to use of additional categories. Correlations between improvements in Body Functions and Activities and Participation (regarding capacity and performance), as well as between capacity and performance within Activities and Participation, were approximately 0.4. Rating ICF categories with qualifiers enables the detection of changes in functional profiles of stroke patients who underwent an inpatient rehabilitation programme. :

Examining Urban Expansion Using Multi-Temporal Landsat Imagery: a Case Study of the Montreal Census Metropolitan Area from 1975 TO 2015, Canada

NASA Astrophysics Data System (ADS)

Ma, Lingfei; Zhao, He; Li, Jonathan

2016-06-01

Urban expansion, particularly the movement of residential and commercial land use to sub-urban areas in metropolitan areas, has been considered as a significant signal of regional economic development. In 1970s, the economic centre of Canada moved from Montreal to Toronto. Since some previous research have been focused on the urbanization process in Greater Toronto Area (GTA), it is significant to conduct research in its counterpart. This study evaluates urban expansion process in Montréal census metropolitan area (CMA), Canada, between 1975 and 2015 using satellite images and socio-economic data. Spatial and temporal dynamic information of urbanization process was quantified using Landsat imagery, supervised classification algorithms and the post-classification change detection technique. Accuracy of the Landsat-derived land use classification map ranged from 80% to 97%. The results indicated that continuous growth of built-up areas in the CMA over the study period resulted in a decrease in the area of cultivated land and vegetation. The results showed that urban areas expanded 442 km2 both along major river systems and lakeshores, as well as expanded from urban centres to surrounded areas. The analysis revealed that urban expansion has been largely driven by population growth and economic development. Consequently, the urban expansion maps produced in this research can assist decision-makers to promote sustainable urban development, and forecast potential changes in urbanization growth patterns.

Study Points to Genetic Subtypes of Esophageal Cancer

Cancer.gov

A Cancer Currents blog post about a study by The Cancer Genome Atlas Research Network that identified distinct genetic and molecular changes in esophageal cancers that could improve their classification and identify potential new treatments.

Measuring land-use and land-cover change using the U.S. department of agriculture's cropland data layer: Cautions and recommendations

NASA Astrophysics Data System (ADS)

Lark, Tyler J.; Mueller, Richard M.; Johnson, David M.; Gibbs, Holly K.

2017-10-01

Monitoring agricultural land is important for understanding and managing food production, environmental conservation efforts, and climate change. The United States Department of Agriculture's Cropland Data Layer (CDL), an annual satellite imagery-derived land cover map, has been increasingly used for this application since complete coverage of the conterminous United States became available in 2008. However, the CDL is designed and produced with the intent of mapping annual land cover rather than tracking changes over time, and as a result certain precautions are needed in multi-year change analyses to minimize error and misapplication. We highlight scenarios that require special considerations, suggest solutions to key challenges, and propose a set of recommended good practices and general guidelines for CDL-based land change estimation. We also characterize a problematic issue of crop area underestimation bias within the CDL that needs to be accounted for and corrected when calculating changes to crop and cropland areas. When used appropriately and in conjunction with related information, the CDL is a valuable and effective tool for detecting diverse trends in agriculture. By explicitly discussing the methods and techniques for post-classification measurement of land-cover and land-use change using the CDL, we aim to further stimulate the discourse and continued development of suitable methodologies. Recommendations generated here are intended specifically for the CDL but may be broadly applicable to additional remotely-sensed land cover datasets including the National Land Cover Database (NLCD), Moderate Resolution Imaging Spectroradiometer (MODIS)-based land cover products, and other regional, national, and global land cover classification maps.

Detection of motor imagery of swallow EEG signals based on the dual-tree complex wavelet transform and adaptive model selection

NASA Astrophysics Data System (ADS)

Yang, Huijuan; Guan, Cuntai; Sui Geok Chua, Karen; San Chok, See; Wang, Chuan Chu; Kok Soon, Phua; Tang, Christina Ka Yin; Keng Ang, Kai

2014-06-01

Objective. Detection of motor imagery of hand/arm has been extensively studied for stroke rehabilitation. This paper firstly investigates the detection of motor imagery of swallow (MI-SW) and motor imagery of tongue protrusion (MI-Ton) in an attempt to find a novel solution for post-stroke dysphagia rehabilitation. Detection of MI-SW from a simple yet relevant modality such as MI-Ton is then investigated, motivated by the similarity in activation patterns between tongue movements and swallowing and there being fewer movement artifacts in performing tongue movements compared to swallowing. Approach. Novel features were extracted based on the coefficients of the dual-tree complex wavelet transform to build multiple training models for detecting MI-SW. The session-to-session classification accuracy was boosted by adaptively selecting the training model to maximize the ratio of between-classes distances versus within-class distances, using features of training and evaluation data. Main results. Our proposed method yielded averaged cross-validation (CV) classification accuracies of 70.89% and 73.79% for MI-SW and MI-Ton for ten healthy subjects, which are significantly better than the results from existing methods. In addition, averaged CV accuracies of 66.40% and 70.24% for MI-SW and MI-Ton were obtained for one stroke patient, demonstrating the detectability of MI-SW and MI-Ton from the idle state. Furthermore, averaged session-to-session classification accuracies of 72.08% and 70% were achieved for ten healthy subjects and one stroke patient using the MI-Ton model. Significance. These results and the subjectwise strong correlations in classification accuracies between MI-SW and MI-Ton demonstrated the feasibility of detecting MI-SW from MI-Ton models.

Accessing long-term memory representations during visual change detection.

PubMed

Beck, Melissa R; van Lamsweerde, Amanda E

2011-04-01

In visual change detection tasks, providing a cue to the change location concurrent with the test image (post-cue) can improve performance, suggesting that, without a cue, not all encoded representations are automatically accessed. Our studies examined the possibility that post-cues can encourage the retrieval of representations stored in long-term memory (LTM). Participants detected changes in images composed of familiar objects. Performance was better when the cue directed attention to the post-change object. Supporting the role of LTM in the cue effect, the effect was similar regardless of whether the cue was presented during the inter-stimulus interval, concurrent with the onset of the test image, or after the onset of the test image. Furthermore, the post-cue effect and LTM performance were similarly influenced by encoding time. These findings demonstrate that monitoring the visual world for changes does not automatically engage LTM retrieval.

Land cover mapping after the tsunami event over Nanggroe Aceh Darussalam (NAD) province, Indonesia

NASA Astrophysics Data System (ADS)

Lim, H. S.; MatJafri, M. Z.; Abdullah, K.; Alias, A. N.; Mohd. Saleh, N.; Wong, C. J.; Surbakti, M. S.

2008-03-01

Remote sensing offers an important means of detecting and analyzing temporal changes occurring in our landscape. This research used remote sensing to quantify land use/land cover changes at the Nanggroe Aceh Darussalam (Nad) province, Indonesia on a regional scale. The objective of this paper is to assess the changed produced from the analysis of Landsat TM data. A Landsat TM image was used to develop land cover classification map for the 27 March 2005. Four supervised classifications techniques (Maximum Likelihood, Minimum Distance-to- Mean, Parallelepiped and Parallelepiped with Maximum Likelihood Classifier Tiebreaker classifier) were performed to the satellite image. Training sites and accuracy assessment were needed for supervised classification techniques. The training sites were established using polygons based on the colour image. High detection accuracy (>80%) and overall Kappa (>0.80) were achieved by the Parallelepiped with Maximum Likelihood Classifier Tiebreaker classifier in this study. This preliminary study has produced a promising result. This indicates that land cover mapping can be carried out using remote sensing classification method of the satellite digital imagery.

Evaluation of computed tomography post-processing images in postoperative assessment of Lisfranc injuries compared with plain radiographs.

PubMed

Li, Haobo; Chen, Yanxi; Qiang, Minfei; Zhang, Kun; Jiang, Yuchen; Zhang, Yijie; Jia, Xiaoyang

2017-06-14

The objective of this study is to evaluate the value of computed tomography (CT) post-processing images in postoperative assessment of Lisfranc injuries compared with plain radiographs. A total of 79 cases with closed Lisfranc injuries that were treated with conventional open reduction and internal fixation from January 2010 to June 2016 were analyzed. Postoperative assessment was performed by two independent orthopedic surgeons with both plain radiographs and CT post-processing images. Inter- and intra-observer agreement were analyzed by kappa statistics while the differences between the two postoperative imaging assessments were assessed using the χ 2 test (McNemar's test). Significance was assumed when p < 0.05. Inter- and intra-observer agreement of CT post-processing images was much higher than that of plain radiographs. Non-anatomic reduction was more easily identified in patients with injuries of Myerson classifications A, B1, B2, and C1 using CT post-processing images with overall groups (p < 0.05), and poor internal fixation was also more easily detected in patients with injuries of Myerson classifications A, B1, B2, and C2 using CT post-processing images with overall groups (p < 0.05). CT post-processing images can be more reliable than plain radiographs in the postoperative assessment of reduction and implant placement for Lisfranc injuries.

Filtering big data from social media--Building an early warning system for adverse drug reactions.

PubMed

Yang, Ming; Kiang, Melody; Shang, Wei

2015-04-01

Adverse drug reactions (ADRs) are believed to be a leading cause of death in the world. Pharmacovigilance systems are aimed at early detection of ADRs. With the popularity of social media, Web forums and discussion boards become important sources of data for consumers to share their drug use experience, as a result may provide useful information on drugs and their adverse reactions. In this study, we propose an automated ADR related posts filtering mechanism using text classification methods. In real-life settings, ADR related messages are highly distributed in social media, while non-ADR related messages are unspecific and topically diverse. It is expensive to manually label a large amount of ADR related messages (positive examples) and non-ADR related messages (negative examples) to train classification systems. To mitigate this challenge, we examine the use of a partially supervised learning classification method to automate the process. We propose a novel pharmacovigilance system leveraging a Latent Dirichlet Allocation modeling module and a partially supervised classification approach. We select drugs with more than 500 threads of discussion, and collect all the original posts and comments of these drugs using an automatic Web spidering program as the text corpus. Various classifiers were trained by varying the number of positive examples and the number of topics. The trained classifiers were applied to 3000 posts published over 60 days. Top-ranked posts from each classifier were pooled and the resulting set of 300 posts was reviewed by a domain expert to evaluate the classifiers. Compare to the alternative approaches using supervised learning methods and three general purpose partially supervised learning methods, our approach performs significantly better in terms of precision, recall, and the F measure (the harmonic mean of precision and recall), based on a computational experiment using online discussion threads from Medhelp. Our design provides satisfactory performance in identifying ADR related posts for post-marketing drug surveillance. The overall design of our system also points out a potentially fruitful direction for building other early warning systems that need to filter big data from social media networks. Copyright © 2015 Elsevier Inc. All rights reserved.

Invasive species change detection using artificial neural networks and CASI hyperspectral imagery

USDA-ARS?s Scientific Manuscript database

For monitoring and controlling the extent and intensity of an invasive species, a direct multi-date image classification method was applied in invasive species (saltcedar) change detection in the study area of Lovelock, Nevada. With multi-date Compact Airborne Spectrographic Imager (CASI) hyperspec...

Modulatory effects of ketamine, risperidone and lamotrigine on resting brain perfusion in healthy human subjects.

PubMed

Shcherbinin, Sergey; Doyle, Orla; Zelaya, Fernando O; de Simoni, Sara; Mehta, Mitul A; Schwarz, Adam J

2015-11-01

Resting brain perfusion, measured using the MRI-based arterial spin labelling (ASL) technique, is sensitive to detect central effects of single, clinically effective, doses of pharmacological compounds. However, pharmacological interaction experiments, such as the modulation of one drug response in the presence of another, have not been widely investigated using a task-free ASL approach. We assessed the effects of three psychoactive compounds (ketamine, risperidone and lamotrigine), and their interaction, on resting brain perfusion in healthy human volunteers. A multivariate Gaussian process classification (GPC) and more conventional univariate analyses were applied. The four pre-infusion conditions for each subject comprised risperidone, lamotrigine and two placebo sessions. The two placebo conditions enabled us to evaluate the classification performance in a test-retest setting, in addition to its performance in distinguishing the active oral drugs from placebo (direct effect on brain perfusion). The post ketamine- or saline-infusion scans allowed the effect of ketamine, and its interaction with risperidone and lamotrigine, on brain perfusion to be characterised. The pseudo-continuous ASL measurements of perfusion were sensitive to the effects of ketamine infusion and risperidone. The GPC captured consistent changes in perfusion across the group and contextualised the univariate changes with a larger pattern of regions contributing to accurate discrimination of ketamine from placebo. The findings argue against perfusion changes confounding in the previously described evoked BOLD response to ketamine and emphasise the blockade of the NMDA receptor over neuronal glutamate release in determining the perfusion changes induced by ketamine.

[Automated identification, interpretation and classification of focal changes in the lungs on the images obtained at computed tomography for lung cancer screening].

PubMed

Barchuk, A A; Podolsky, M D; Tarakanov, S A; Kotsyuba, I Yu; Gaidukov, V S; Kuznetsov, V I; Merabishvili, V M; Barchuk, A S; Levchenko, E V; Filochkina, A V; Arseniev, A I

2015-01-01

This review article analyzes data of literature devoted to the description, interpretation and classification of focal (nodal) changes in the lungs detected by computed tomography of the chest cavity. There are discussed possible criteria for determining the most likely of their character--primary and metastatic tumor processes, inflammation, scarring, and autoimmune changes, tuberculosis and others. Identification of the most characteristic, reliable and statistically significant evidences of a variety of pathological processes in the lungs including the use of modern computer-aided detection and diagnosis of sites will optimize the diagnostic measures and ensure processing of a large volume of medical data in a short time.

Remote sensing change detection methods to track deforestation and growth in threatened rainforests in Madre de Dios, Peru

USGS Publications Warehouse

Shermeyer, Jacob S.; Haack, Barry N.

2015-01-01

Two forestry-change detection methods are described, compared, and contrasted for estimating deforestation and growth in threatened forests in southern Peru from 2000 to 2010. The methods used in this study rely on freely available data, including atmospherically corrected Landsat 5 Thematic Mapper and Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation continuous fields (VCF). The two methods include a conventional supervised signature extraction method and a unique self-calibrating method called MODIS VCF guided forest/nonforest (FNF) masking. The process chain for each of these methods includes a threshold classification of MODIS VCF, training data or signature extraction, signature evaluation, k-nearest neighbor classification, analyst-guided reclassification, and postclassification image differencing to generate forest change maps. Comparisons of all methods were based on an accuracy assessment using 500 validation pixels. Results of this accuracy assessment indicate that FNF masking had a 5% higher overall accuracy and was superior to conventional supervised classification when estimating forest change. Both methods succeeded in classifying persistently forested and nonforested areas, and both had limitations when classifying forest change.

Classification of multispectral or hyperspectral satellite imagery using clustering of sparse approximations on sparse representations in learned dictionaries obtained using efficient convolutional sparse coding

DOEpatents

Moody, Daniela; Wohlberg, Brendt

2018-01-02

An approach for land cover classification, seasonal and yearly change detection and monitoring, and identification of changes in man-made features may use a clustering of sparse approximations (CoSA) on sparse representations in learned dictionaries. The learned dictionaries may be derived using efficient convolutional sparse coding to build multispectral or hyperspectral, multiresolution dictionaries that are adapted to regional satellite image data. Sparse image representations of images over the learned dictionaries may be used to perform unsupervised k-means clustering into land cover categories. The clustering process behaves as a classifier in detecting real variability. This approach may combine spectral and spatial textural characteristics to detect geologic, vegetative, hydrologic, and man-made features, as well as changes in these features over time.

Multidecadal Rates of Disturbance- and Climate Change-Induced Land Cover Change in Arctic and Boreal Ecosystems over Western Canada and Alaska Inferred from Dense Landsat Time Series

NASA Astrophysics Data System (ADS)

Wang, J.; Sulla-menashe, D. J.; Woodcock, C. E.; Sonnentag, O.; Friedl, M. A.

2017-12-01

Rapid climate change in arctic and boreal ecosystems is driving changes to land cover composition, including woody expansion in the arctic tundra, successional shifts following boreal fires, and thaw-induced wetland expansion and forest collapse along the southern limit of permafrost. The impacts of these land cover transformations on the physical climate and the carbon cycle are increasingly well-documented from field and model studies, but there have been few attempts to empirically estimate rates of land cover change at decadal time scale and continental spatial scale. Previous studies have used too coarse spatial resolution or have been too limited in temporal range to enable broad multi-decadal assessment of land cover change. As part of NASA's Arctic Boreal Vulnerability Experiment (ABoVE), we are using dense time series of Landsat remote sensing data to map disturbances and classify land cover types across the ABoVE extended domain (spanning western Canada and Alaska) over the last three decades (1982-2014) at 30 m resolution. We utilize regionally-complete and repeated acquisition high-resolution (<2 m) DigitalGlobe imagery to generate training data from across the region that follows a nested, hierarchical classification scheme encompassing plant functional type and cover density, understory type, wetland status, and land use. Additionally, we crosswalk plot-level field data into our scheme for additional high quality training sites. We use the Continuous Change Detection and Classification algorithm to estimate land cover change dates and temporal-spectral features in the Landsat data. These features are used to train random forest classification models and map land cover and analyze land cover change processes, focusing primarily on tundra "shrubification", post-fire succession, and boreal wetland expansion. We will analyze the high resolution data based on stratified random sampling of our change maps to validate and assess the accuracy of our model predictions. In this paper, we present initial results from this effort, including sub-regional analyses focused on several key areas, such as the Taiga Plains and the Southern Arctic ecozones, to calibrate our random forest models and assess results.

SU-F-R-17: Advancing Glioblastoma Multiforme (GBM) Recurrence Detection with MRI Image Texture Feature Extraction and Machine Learning

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

Yu, V; Ruan, D; Nguyen, D

Purpose: To test the potential of early Glioblastoma Multiforme (GBM) recurrence detection utilizing image texture pattern analysis in serial MR images post primary treatment intervention. Methods: MR image-sets of six time points prior to the confirmed recurrence diagnosis of a GBM patient were included in this study, with each time point containing T1 pre-contrast, T1 post-contrast, T2-Flair, and T2-TSE images. Eight Gray-level co-occurrence matrix (GLCM) texture features including Contrast, Correlation, Dissimilarity, Energy, Entropy, Homogeneity, Sum-Average, and Variance were calculated from all images, resulting in a total of 32 features at each time point. A confirmed recurrent volume was contoured, alongmore » with an adjacent non-recurrent region-of-interest (ROI) and both volumes were propagated to all prior time points via deformable image registration. A support vector machine (SVM) with radial-basis-function kernels was trained on the latest time point prior to the confirmed recurrence to construct a model for recurrence classification. The SVM model was then applied to all prior time points and the volumes classified as recurrence were obtained. Results: An increase in classified volume was observed over time as expected. The size of classified recurrence maintained at a stable level of approximately 0.1 cm{sup 3} up to 272 days prior to confirmation. Noticeable volume increase to 0.44 cm{sup 3} was demonstrated at 96 days prior, followed by significant increase to 1.57 cm{sup 3} at 42 days prior. Visualization of the classified volume shows the merging of recurrence-susceptible region as the volume change became noticeable. Conclusion: Image texture pattern analysis in serial MR images appears to be sensitive to detecting the recurrent GBM a long time before the recurrence is confirmed by a radiologist. The early detection may improve the efficacy of targeted intervention including radiosurgery. More patient cases will be included to create a generalizable classification model applicable to a larger patient cohort. NIH R43CA183390 and R01CA188300.NSF Graduate Research Fellowship DGE-1144087.« less

The Application of Remote Sensing Data to GIS Studies of Land Use, Land Cover, and Vegetation Mapping in the State of Hawaii

NASA Technical Reports Server (NTRS)

Hogan, Christine A.

1996-01-01

A land cover-vegetation map with a base classification system for remote sensing use in a tropical island environment was produced of the island of Hawaii for the State of Hawaii to evaluate whether or not useful land cover information can be derived from Landsat TM data. In addition, an island-wide change detection mosaic combining a previously created 1977 MSS land classification with the TM-based classification was produced. In order to reach the goal of transferring remote sensing technology to State of Hawaii personnel, a pilot project was conducted while training State of Hawaii personnel in remote sensing technology and classification systems. Spectral characteristics of young island land cover types were compared to determine if there are differences in vegetation types on lava, vegetation types on soils, and barren lava from soils, and if they can be detected remotely, based on differences in pigments detecting plant physiognomic type, health, stress at senescence, heat, moisture level, and biomass. Geographic information systems (GIS) and global positioning systems (GPS) were used to assist in image rectification and classification. GIS was also used to produce large-format color output maps. An interactive GIS program was written to provide on-line access to scanned photos taken at field sites. The pilot project found Landsat TM to be a credible source of land cover information for geologically young islands, and TM data bands are effective in detecting spectral characteristics of different land cover types through remote sensing. Large agriculture field patterns were resolved and mapped successfully from wildland vegetation, but small agriculture field patterns were not. Additional processing was required to work with the four TM scenes from two separate orbits which span three years, including El Nino and drought dates. Results of the project emphasized the need for further land cover and land use processing and research. Change in vegetation composition was noted in the change detection image.

Quantifying tree mortality in a mixed species woodland using multitemporal high spatial resolution satellite imagery

USGS Publications Warehouse

Garrity, Steven R.; Allen, Craig D.; Brumby, Steven P.; Gangodagamage, Chandana; McDowell, Nate G.; Cai, D. Michael

2013-01-01

Widespread tree mortality events have recently been observed in several biomes. To effectively quantify the severity and extent of these events, tools that allow for rapid assessment at the landscape scale are required. Past studies using high spatial resolution satellite imagery have primarily focused on detecting green, red, and gray tree canopies during and shortly after tree damage or mortality has occurred. However, detecting trees in various stages of death is not always possible due to limited availability of archived satellite imagery. Here we assess the capability of high spatial resolution satellite imagery for tree mortality detection in a southwestern U.S. mixed species woodland using archived satellite images acquired prior to mortality and well after dead trees had dropped their leaves. We developed a multistep classification approach that uses: supervised masking of non-tree image elements; bi-temporal (pre- and post-mortality) differencing of normalized difference vegetation index (NDVI) and red:green ratio (RGI); and unsupervised multivariate clustering of pixels into live and dead tree classes using a Gaussian mixture model. Classification accuracies were improved in a final step by tuning the rules of pixel classification using the posterior probabilities of class membership obtained from the Gaussian mixture model. Classifications were produced for two images acquired post-mortality with overall accuracies of 97.9% and 98.5%, respectively. Classified images were combined with land cover data to characterize the spatiotemporal characteristics of tree mortality across areas with differences in tree species composition. We found that 38% of tree crown area was lost during the drought period between 2002 and 2006. The majority of tree mortality during this period was concentrated in piñon-juniper (Pinus edulis-Juniperus monosperma) woodlands. An additional 20% of the tree canopy died or was removed between 2006 and 2011, primarily in areas experiencing wildfire and management activity. -Our results demonstrate that unsupervised clustering of bi-temporal NDVI and RGI differences can be used to detect tree mortality resulting from numerous causes and in several forest cover types.

Spatiotemporal Change Detection in Forest Cover Dynamics Along Landslide Susceptible Region of Karakoram Highway, Pakistan

NASA Astrophysics Data System (ADS)

Rashid, Barira; Iqbal, Javed

2018-04-01

Forest Cover dynamics and its understanding is essential for a country's social, environmental, and political engagements. This research provides a methodical approach for the assessment of forest cover along Karakoram Highway. It has great ecological and economic significance because it's a part of China-Pakistan Economic Corridor. Landsat 4, 5 TM, Landsat 7 ETM and Landsat 8 OLI imagery for the years 1990, 2000, 2010 and 2016 respectively were subjected to supervised classification in ArcMap 10.5 to identify forest change. The study area was categorized into five major land use land cover classes i.e., Forest, vegetation, urban, open land and snow cover. Results from post classification forest cover change maps illustrated notable decrease of almost 26 % forest cover over the time period of 26 years. The accuracy assessment revealed the kappa coefficients 083, 0.78, 0.77 and 0.85, respectively. Major reason for this change is an observed replacement of native forest cover with urban areas (12.5 %) and vegetation (18.6 %) However, there is no significant change in the reserved forests along the study area that contributes only 2.97 % of the total forest cover. The extensive forest degradation and risk prone topography of the region has increased the environmental risk of landslides. Hence, effective policies and forest management is needed to protect not only the environmental and aesthetic benefits of the forest cover but also to manage the disaster risks. Apart from the forest assessment, this research gives an insight of land cover dynamics, along with causes and consequences, thereby showing the forest degradation hotspots.

Detecting depression stigma on social media: A linguistic analysis.

PubMed

Li, Ang; Jiao, Dongdong; Zhu, Tingshao

2018-05-01

Efficient detection of depression stigma in mass media is important for designing effective stigma reduction strategies. Using linguistic analysis methods, this paper aims to build computational models for detecting stigma expressions in Chinese social media posts (Sina Weibo). A total of 15,879 Weibo posts with keywords were collected and analyzed. First, a content analysis was conducted on all 15,879 posts to determine whether each of them reflected depression stigma or not. Second, using four algorithms (Simple Logistic Regression, Multilayer Perceptron Neural Networks, Support Vector Machine, and Random Forest), two groups of classification models were built based on selected linguistic features; one for differentiating between posts with and without depression stigma, and one for differentiating among posts with three specific types of depression stigma. First, 967 of 15,879 posts (6.09%) indicated depression stigma. 39.30%, 15.82%, and 14.99% of them endorsed the stigmatizing view that "People with depression are unpredictable", "Depression is a sign of personal weakness", and "Depression is not a real medical illness", respectively. Second, the highest F-Measure value for differentiating between stigma and non-stigma reached 75.2%. The highest F-Measure value for differentiating among three specific types of stigma reached 86.2%. Due to the limited and imbalanced dataset of Chinese Weibo posts, the findings of this study might have limited generalizability. This paper confirms that incorporating linguistic analysis methods into online detection of stigma can be beneficial to improve the performance of stigma reduction programs. Copyright © 2018 Elsevier B.V. All rights reserved.

Full genome virus detection in fecal samples using sensitive nucleic acid preparation, deep sequencing, and a novel iterative sequence classification algorithm.

PubMed

Cotten, Matthew; Oude Munnink, Bas; Canuti, Marta; Deijs, Martin; Watson, Simon J; Kellam, Paul; van der Hoek, Lia

2014-01-01

We have developed a full genome virus detection process that combines sensitive nucleic acid preparation optimised for virus identification in fecal material with Illumina MiSeq sequencing and a novel post-sequencing virus identification algorithm. Enriched viral nucleic acid was converted to double-stranded DNA and subjected to Illumina MiSeq sequencing. The resulting short reads were processed with a novel iterative Python algorithm SLIM for the identification of sequences with homology to known viruses. De novo assembly was then used to generate full viral genomes. The sensitivity of this process was demonstrated with a set of fecal samples from HIV-1 infected patients. A quantitative assessment of the mammalian, plant, and bacterial virus content of this compartment was generated and the deep sequencing data were sufficient to assembly 12 complete viral genomes from 6 virus families. The method detected high levels of enteropathic viruses that are normally controlled in healthy adults, but may be involved in the pathogenesis of HIV-1 infection and will provide a powerful tool for virus detection and for analyzing changes in the fecal virome associated with HIV-1 progression and pathogenesis.

Full Genome Virus Detection in Fecal Samples Using Sensitive Nucleic Acid Preparation, Deep Sequencing, and a Novel Iterative Sequence Classification Algorithm

PubMed Central

Cotten, Matthew; Oude Munnink, Bas; Canuti, Marta; Deijs, Martin; Watson, Simon J.; Kellam, Paul; van der Hoek, Lia

2014-01-01

We have developed a full genome virus detection process that combines sensitive nucleic acid preparation optimised for virus identification in fecal material with Illumina MiSeq sequencing and a novel post-sequencing virus identification algorithm. Enriched viral nucleic acid was converted to double-stranded DNA and subjected to Illumina MiSeq sequencing. The resulting short reads were processed with a novel iterative Python algorithm SLIM for the identification of sequences with homology to known viruses. De novo assembly was then used to generate full viral genomes. The sensitivity of this process was demonstrated with a set of fecal samples from HIV-1 infected patients. A quantitative assessment of the mammalian, plant, and bacterial virus content of this compartment was generated and the deep sequencing data were sufficient to assembly 12 complete viral genomes from 6 virus families. The method detected high levels of enteropathic viruses that are normally controlled in healthy adults, but may be involved in the pathogenesis of HIV-1 infection and will provide a powerful tool for virus detection and for analyzing changes in the fecal virome associated with HIV-1 progression and pathogenesis. PMID:24695106

Object memory and change detection: dissociation as a function of visual and conceptual similarity.

PubMed

Yeh, Yei-Yu; Yang, Cheng-Ta

2008-01-01

People often fail to detect a change between two visual scenes, a phenomenon referred to as change blindness. This study investigates how a post-change object's similarity to the pre-change object influences memory of the pre-change object and affects change detection. The results of Experiment 1 showed that similarity lowered detection sensitivity but did not affect the speed of identifying the pre-change object, suggesting that similarity between the pre- and post-change objects does not degrade the pre-change representation. Identification speed for the pre-change object was faster than naming the new object regardless of detection accuracy. Similarity also decreased detection sensitivity in Experiment 2 but improved the recognition of the pre-change object under both correct detection and detection failure. The similarity effect on recognition was greatly reduced when 20% of each pre-change stimulus was masked by random dots in Experiment 3. Together the results suggest that the level of pre-change representation under detection failure is equivalent to the level under correct detection and that the pre-change representation is almost complete. Similarity lowers detection sensitivity but improves explicit access in recognition. Dissociation arises between recognition and change detection as the two judgments rely on the match-to-mismatch signal and mismatch-to-match signal, respectively.

Damage diagnosis algorithm using a sequential change point detection method with an unknown distribution for damage

NASA Astrophysics Data System (ADS)

Noh, Hae Young; Rajagopal, Ram; Kiremidjian, Anne S.

2012-04-01

This paper introduces a damage diagnosis algorithm for civil structures that uses a sequential change point detection method for the cases where the post-damage feature distribution is unknown a priori. This algorithm extracts features from structural vibration data using time-series analysis and then declares damage using the change point detection method. The change point detection method asymptotically minimizes detection delay for a given false alarm rate. The conventional method uses the known pre- and post-damage feature distributions to perform a sequential hypothesis test. In practice, however, the post-damage distribution is unlikely to be known a priori. Therefore, our algorithm estimates and updates this distribution as data are collected using the maximum likelihood and the Bayesian methods. We also applied an approximate method to reduce the computation load and memory requirement associated with the estimation. The algorithm is validated using multiple sets of simulated data and a set of experimental data collected from a four-story steel special moment-resisting frame. Our algorithm was able to estimate the post-damage distribution consistently and resulted in detection delays only a few seconds longer than the delays from the conventional method that assumes we know the post-damage feature distribution. We confirmed that the Bayesian method is particularly efficient in declaring damage with minimal memory requirement, but the maximum likelihood method provides an insightful heuristic approach.

Combined optical coherence tomography and optical coherence elastography for glomerulonephritis classification

NASA Astrophysics Data System (ADS)

Liu, Chih-Hao; Du, Yong; Singh, Manmohan; Wu, Chen; Han, Zhaolong; Li, Jiasong; Mohammadzai, Qais; Raghunathan, Raksha; Hsu, Thomas; Noorani, Shezaan; Chang, Anthony; Mohan, Chandra; Larin, Kirill V.

2016-03-01

Acute Glomerulonephritis caused by anti-glomerular basement membrane disease has a high mortality due to delayed diagnosis. Thus, an accurate and early diagnosis is critical for preserving renal function. Currently, blood, urine, and tissue-based diagnoses can be time consuming, while ultrasound and CT imaging have relatively low spatial resolution. Optical coherence tomography (OCT) is a noninvasive imaging technique that provides superior spatial resolution (micron scale) as compared to ultrasound and CT. Pathological changes in tissue properties can be detected based on the optical metrics analyzed from the OCT signal, such as optical attenuation and speckle variance. Moreover, OCT does not rely on ionizing radiation as with CT imaging. In addition to structural changes, the elasticity of the kidney can significantly change due to nephritis. In this work, we utilized OCT to detect the difference in tissue properties between healthy and nephritic murine kidneys. Although OCT imaging could identify the diseased tissue, classification accuracy using only optical metrics was clinically inadequate. By combining optical metrics with elasticity, the classification accuracy improved from 76% to 95%. These results show that OCT combined with OCE can be potentially useful for nephritis detection.

Permutation Entropy and Signal Energy Increase the Accuracy of Neuropathic Change Detection in Needle EMG

PubMed Central

2018-01-01

Background and Objective. Needle electromyography can be used to detect the number of changes and morphological changes in motor unit potentials of patients with axonal neuropathy. General mathematical methods of pattern recognition and signal analysis were applied to recognize neuropathic changes. This study validates the possibility of extending and refining turns-amplitude analysis using permutation entropy and signal energy. Methods. In this study, we examined needle electromyography in 40 neuropathic individuals and 40 controls. The number of turns, amplitude between turns, signal energy, and “permutation entropy” were used as features for support vector machine classification. Results. The obtained results proved the superior classification performance of the combinations of all of the above-mentioned features compared to the combinations of fewer features. The lowest accuracy from the tested combinations of features had peak-ratio analysis. Conclusion. Using the combination of permutation entropy with signal energy, number of turns and mean amplitude in SVM classification can be used to refine the diagnosis of polyneuropathies examined by needle electromyography. PMID:29606959

Comparing automated classification and digitization approaches to detect change in eelgrass bed extent during restoration of a large river delta

USGS Publications Warehouse

Davenport, Anna Elizabeth; Davis, Jerry D.; Woo, Isa; Grossman, Eric; Barham, Jesse B.; Ellings, Christopher S.; Takekawa, John Y.

2017-01-01

Native eelgrass (Zostera marina) is an important contributor to ecosystem services that supplies cover for juvenile fish, supports a variety of invertebrate prey resources for fish and waterbirds, provides substrate for herring roe consumed by numerous fish and birds, helps stabilize sediment, and sequesters organic carbon. Seagrasses are in decline globally, and monitoring changes in their growth and extent is increasingly valuable to determine impacts from large-scale estuarine restoration and inform blue carbon mapping initiatives. Thus, we examined the efficacy of two remote sensing mapping methods with high-resolution (0.5 m pixel size) color near infrared imagery with ground validation to assess change following major tidal marsh restoration. Automated classification of false color aerial imagery and digitized polygons documented a slight decline in eelgrass area directly after restoration followed by an increase two years later. Classification of sparse and low to medium density eelgrass was confounded in areas with algal cover, however large dense patches of eelgrass were well delineated. Automated classification of aerial imagery from unsupervised and supervised methods provided reasonable accuracies of 73% and hand-digitizing polygons from the same imagery yielded similar results. Visual clues for hand digitizing from the high-resolution imagery provided as reliable a map of dense eelgrass extent as automated image classification. We found that automated classification had no advantages over manual digitization particularly because of the limitations of detecting eelgrass with only three bands of imagery and near infrared.

Mapping Sub-Antarctic Cushion Plants Using Random Forests to Combine Very High Resolution Satellite Imagery and Terrain Modelling

PubMed Central

Bricher, Phillippa K.; Lucieer, Arko; Shaw, Justine; Terauds, Aleks; Bergstrom, Dana M.

2013-01-01

Monitoring changes in the distribution and density of plant species often requires accurate and high-resolution baseline maps of those species. Detecting such change at the landscape scale is often problematic, particularly in remote areas. We examine a new technique to improve accuracy and objectivity in mapping vegetation, combining species distribution modelling and satellite image classification on a remote sub-Antarctic island. In this study, we combine spectral data from very high resolution WorldView-2 satellite imagery and terrain variables from a high resolution digital elevation model to improve mapping accuracy, in both pixel- and object-based classifications. Random forest classification was used to explore the effectiveness of these approaches on mapping the distribution of the critically endangered cushion plant Azorella macquariensis Orchard (Apiaceae) on sub-Antarctic Macquarie Island. Both pixel- and object-based classifications of the distribution of Azorella achieved very high overall validation accuracies (91.6–96.3%, κ = 0.849–0.924). Both two-class and three-class classifications were able to accurately and consistently identify the areas where Azorella was absent, indicating that these maps provide a suitable baseline for monitoring expected change in the distribution of the cushion plants. Detecting such change is critical given the threats this species is currently facing under altering environmental conditions. The method presented here has applications to monitoring a range of species, particularly in remote and isolated environments. PMID:23940805

Post-classification approaches to estimating change in forest area using remotely sense auxiliary data.

Treesearch

Ronald E. McRoberts

2014-01-01

Multiple remote sensing-based approaches to estimating gross afforestation, gross deforestation, and net deforestation are possible. However, many of these approaches have severe data requirements in the form of long time series of remotely sensed data and/or large numbers of observations of land cover change to train classifiers and assess the accuracy of...

Hybrid Optimization of Object-Based Classification in High-Resolution Images Using Continous ANT Colony Algorithm with Emphasis on Building Detection

NASA Astrophysics Data System (ADS)

Tamimi, E.; Ebadi, H.; Kiani, A.

2017-09-01

Automatic building detection from High Spatial Resolution (HSR) images is one of the most important issues in Remote Sensing (RS). Due to the limited number of spectral bands in HSR images, using other features will lead to improve accuracy. By adding these features, the presence probability of dependent features will be increased, which leads to accuracy reduction. In addition, some parameters should be determined in Support Vector Machine (SVM) classification. Therefore, it is necessary to simultaneously determine classification parameters and select independent features according to image type. Optimization algorithm is an efficient method to solve this problem. On the other hand, pixel-based classification faces several challenges such as producing salt-paper results and high computational time in high dimensional data. Hence, in this paper, a novel method is proposed to optimize object-based SVM classification by applying continuous Ant Colony Optimization (ACO) algorithm. The advantages of the proposed method are relatively high automation level, independency of image scene and type, post processing reduction for building edge reconstruction and accuracy improvement. The proposed method was evaluated by pixel-based SVM and Random Forest (RF) classification in terms of accuracy. In comparison with optimized pixel-based SVM classification, the results showed that the proposed method improved quality factor and overall accuracy by 17% and 10%, respectively. Also, in the proposed method, Kappa coefficient was improved by 6% rather than RF classification. Time processing of the proposed method was relatively low because of unit of image analysis (image object). These showed the superiority of the proposed method in terms of time and accuracy.

Change classification in SAR time series: a functional approach

NASA Astrophysics Data System (ADS)

Boldt, Markus; Thiele, Antje; Schulz, Karsten; Hinz, Stefan

2017-10-01

Change detection represents a broad field of research in SAR remote sensing, consisting of many different approaches. Besides the simple recognition of change areas, the analysis of type, category or class of the change areas is at least as important for creating a comprehensive result. Conventional strategies for change classification are based on supervised or unsupervised landuse / landcover classifications. The main drawback of such approaches is that the quality of the classification result directly depends on the selection of training and reference data. Additionally, supervised processing methods require an experienced operator who capably selects the training samples. This training step is not necessary when using unsupervised strategies, but nevertheless meaningful reference data must be available for identifying the resulting classes. Consequently, an experienced operator is indispensable. In this study, an innovative concept for the classification of changes in SAR time series data is proposed. Regarding the drawbacks of traditional strategies given above, it copes without using any training data. Moreover, the method can be applied by an operator, who does not have detailed knowledge about the available scenery yet. This knowledge is provided by the algorithm. The final step of the procedure, which main aspect is given by the iterative optimization of an initial class scheme with respect to the categorized change objects, is represented by the classification of these objects to the finally resulting classes. This assignment step is subject of this paper.

Earthquake Building Damage Mapping Based on Feature Analyzing Method from Synthetic Aperture Radar Data

NASA Astrophysics Data System (ADS)

An, L.; Zhang, J.; Gong, L.

2018-04-01

Playing an important role in gathering information of social infrastructure damage, Synthetic Aperture Radar (SAR) remote sensing is a useful tool for monitoring earthquake disasters. With the wide application of this technique, a standard method, comparing post-seismic to pre-seismic data, become common. However, multi-temporal SAR processes, are not always achievable. To develop a post-seismic data only method for building damage detection, is of great importance. In this paper, the authors are now initiating experimental investigation to establish an object-based feature analysing classification method for building damage recognition.

Adaptive sequential Bayesian classification using Page's test

NASA Astrophysics Data System (ADS)

Lynch, Robert S., Jr.; Willett, Peter K.

2002-03-01

In this paper, the previously introduced Mean-Field Bayesian Data Reduction Algorithm is extended for adaptive sequential hypothesis testing utilizing Page's test. In general, Page's test is well understood as a method of detecting a permanent change in distribution associated with a sequence of observations. However, the relationship between detecting a change in distribution utilizing Page's test with that of classification and feature fusion is not well understood. Thus, the contribution of this work is based on developing a method of classifying an unlabeled vector of fused features (i.e., detect a change to an active statistical state) as quickly as possible given an acceptable mean time between false alerts. In this case, the developed classification test can be thought of as equivalent to performing a sequential probability ratio test repeatedly until a class is decided, with the lower log-threshold of each test being set to zero and the upper log-threshold being determined by the expected distance between false alerts. It is of interest to estimate the delay (or, related stopping time) to a classification decision (the number of time samples it takes to classify the target), and the mean time between false alerts, as a function of feature selection and fusion by the Mean-Field Bayesian Data Reduction Algorithm. Results are demonstrated by plotting the delay to declaring the target class versus the mean time between false alerts, and are shown using both different numbers of simulated training data and different numbers of relevant features for each class.

A Comparison of RF-DNA Fingerprinting Using High/Low Value Receivers with ZigBee Devices

DTIC Science & Technology

2014-03-27

99) Device Classification using Hybrid Cross-Receiver model and USRP only testing . 54 V. Conclusion This chapter provides a summary of reseach ...Finally, when developing a Hybrid Cross-Receiver model using fingerprints from both receivers, testing with PXIe-only fingerprints proved to be the most...22 3.2 Post -Collection Processing . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.2.1 Burst Detection

Object-based change detection: dimension of damage in residential areas of Abu Suruj, Sudan

NASA Astrophysics Data System (ADS)

Demharter, Timo; Michel, Ulrich; Ehlers, Manfred; Reinartz, Peter

2011-11-01

Given the importance of Change Detection, especially in the field of crisis management, this paper discusses the advantage of object-based Change Detection. This project and the used methods give an opportunity to coordinate relief actions strategically. The principal objective of this project was to develop an algorithm which allows to detect rapidly damaged and destroyed buildings in the area of Abu Suruj. This Sudanese village is located in West-Darfur and has become the victim of civil war. The software eCognition Developer was used to per-form an object-based Change Detection on two panchromatic Quickbird 2 images from two different time slots. The first image shows the area before, the second image shows the area after the massacres in this region. Seeking a classification for the huts of the Sudanese town Abu Suruj was reached by first segmenting the huts and then classifying them on the basis of geo-metrical and brightness-related values. The huts were classified as "new", "destroyed" and "preserved" with the help of a automated algorithm. Finally the results were presented in the form of a map which displays the different conditions of the huts. The accuracy of the project is validated by an accuracy assessment resulting in an Overall Classification Accuracy of 90.50 percent. These change detection results allow aid organizations to provide quick and efficient help where it is needed the most.

A new classification of post-sternotomy dehiscence

PubMed Central

Anger, Jaime; Dantas, Daniel Chagas; Arnoni, Renato Tambellini; Farsky, Pedro Sílvio

2015-01-01

The dehiscence after median transesternal sternotomy used as surgical access for cardiac surgery is one of its complications and it increases the patient's morbidity and mortality. A variety of surgical techniques were recently described resulting to the need of a classification bringing a measure of objectivity to the management of these complex and dangerous wounds. The different related classifications are based in the primary causal infection, but recently the anatomical description of the wound including the deepness and the vertical extension showed to be more useful. We propose a new classification based only on the anatomical changes following sternotomy dehiscence and chronic wound formation separating it in four types according to the deepness and in two sub-groups according to the vertical extension based on the inferior insertion of the pectoralis major muscle. PMID:25859875

A Community-Based Exergaming Physical Activity Program Improves Readiness-to-Change and Self-Efficacy Among Rural-Residing Older Adults.

PubMed

Sowle, Ashleigh J; Francis, Sarah L; Margrett, Jennifer A; Shelley, Mack C; Franke, Warren D

2017-07-01

Rural-residing older adults (OA) are not meeting physical activity (PA) recommendations, such that identifying methods of increasing PA among OA remains an ongoing challenge. This study evaluated the effect of a community-based exergaming program on PA readiness-to-change and self-efficacy among rural-residing OA (n = 265). There was a significant (p = .008) increase in readiness-to-change classification from PRE to POST. Significant increases in self-efficacy, or confidence in their ability to be physically active for a prescribed period of time, were detected for 35 (p = .011) and 40 min (p = .035) of continuous PA. PA self-efficacy change for 35 min of continuous PA (F [3,137] = 3.973, p = .010) and 40 min of continuous PA (F [3,137) = 2.893, p = .038) were influenced by the interaction between PRE self-reported health and PRE PA readiness-to-change levels. Results suggest that an exergaming-themed PA intervention is effective at increasing PA participation and self-efficacy for PA among rural-residing OA.

A new tool for post-AGB SED classification

NASA Astrophysics Data System (ADS)

Bendjoya, P.; Suarez, O.; Galluccio, L.; Michel, O.

We present the results of an unsupervised classification method applied on a set of 344 spectral energy distributions (SED) of post-AGB stars extracted from the Torun catalogue of Galactic post-AGB stars. This method aims to find a new unbiased method for post-AGB star classification based on the information contained in the IR region of the SED (fluxes, IR excess, colours). We used the data from IRAS and MSX satellites, and from the 2MASS survey. We applied a classification method based on the construction of the dataset of a minimal spanning tree (MST) with the Prim's algorithm. In order to build this tree, different metrics have been tested on both flux and color indices. Our method is able to classify the set of 344 post-AGB stars in 9 distinct groups according to their SEDs.

Acquisition and processing of advanced sensor data for ERW and UXO detection and classification

NASA Astrophysics Data System (ADS)

Schultz, Gregory M.; Keranen, Joe; Miller, Jonathan S.; Shubitidze, Fridon

2014-06-01

The remediation of explosive remnants of war (ERW) and associated unexploded ordnance (UXO) has seen improvements through the injection of modern technological advances and streamlined standard operating procedures. However, reliable and cost-effective detection and geophysical mapping of sites contaminated with UXO such as cluster munitions, abandoned ordnance, and improvised explosive devices rely on the ability to discriminate hazardous items from metallic clutter. In addition to anthropogenic clutter, handheld and vehicle-based metal detector systems are plagued by natural geologic and environmental noise in many post conflict areas. We present new and advanced electromagnetic induction (EMI) technologies including man-portable and towed EMI arrays and associated data processing software. While these systems feature vastly different form factors and transmit-receive configurations, they all exhibit several fundamental traits that enable successful classification of EMI anomalies. Specifically, multidirectional sampling of scattered magnetic fields from targets and corresponding high volume of unique data provide rich information for extracting useful classification features for clutter rejection analysis. The quality of classification features depends largely on the extent to which the data resolve unique physics-based parameters. To date, most of the advanced sensors enable high quality inversion by producing data that are extremely rich in spatial content through multi-angle illumination and multi-point reception.

Identifying key hospital service quality factors in online health communities.

PubMed

Jung, Yuchul; Hur, Cinyoung; Jung, Dain; Kim, Minki

2015-04-07

The volume of health-related user-created content, especially hospital-related questions and answers in online health communities, has rapidly increased. Patients and caregivers participate in online community activities to share their experiences, exchange information, and ask about recommended or discredited hospitals. However, there is little research on how to identify hospital service quality automatically from the online communities. In the past, in-depth analysis of hospitals has used random sampling surveys. However, such surveys are becoming impractical owing to the rapidly increasing volume of online data and the diverse analysis requirements of related stakeholders. As a solution for utilizing large-scale health-related information, we propose a novel approach to identify hospital service quality factors and overtime trends automatically from online health communities, especially hospital-related questions and answers. We defined social media-based key quality factors for hospitals. In addition, we developed text mining techniques to detect such factors that frequently occur in online health communities. After detecting these factors that represent qualitative aspects of hospitals, we applied a sentiment analysis to recognize the types of recommendations in messages posted within online health communities. Korea's two biggest online portals were used to test the effectiveness of detection of social media-based key quality factors for hospitals. To evaluate the proposed text mining techniques, we performed manual evaluations on the extraction and classification results, such as hospital name, service quality factors, and recommendation types using a random sample of messages (ie, 5.44% (9450/173,748) of the total messages). Service quality factor detection and hospital name extraction achieved average F1 scores of 91% and 78%, respectively. In terms of recommendation classification, performance (ie, precision) is 78% on average. Extraction and classification performance still has room for improvement, but the extraction results are applicable to more detailed analysis. Further analysis of the extracted information reveals that there are differences in the details of social media-based key quality factors for hospitals according to the regions in Korea, and the patterns of change seem to accurately reflect social events (eg, influenza epidemics). These findings could be used to provide timely information to caregivers, hospital officials, and medical officials for health care policies.

Land cover change detection using a GIS-guided, feature-based classification of Landsat thematic mapper data. [Geographic Information System

NASA Technical Reports Server (NTRS)

Enslin, William R.; Ton, Jezching; Jain, Anil

1987-01-01

Landsat TM data were combined with land cover and planimetric data layers contained in the State of Michigan's geographic information system (GIS) to identify changes in forestlands, specifically new oil/gas wells. A GIS-guided feature-based classification method was developed. The regions extracted by the best image band/operator combination were studied using a set of rules based on the characteristics of the GIS oil/gas pads.

Breast cancer detection in rotational thermography images using texture features

NASA Astrophysics Data System (ADS)

Francis, Sheeja V.; Sasikala, M.; Bhavani Bharathi, G.; Jaipurkar, Sandeep D.

2014-11-01

Breast cancer is a major cause of mortality in young women in the developing countries. Early diagnosis is the key to improve survival rate in cancer patients. Breast thermography is a diagnostic procedure that non-invasively images the infrared emissions from breast surface to aid in the early detection of breast cancer. Due to limitations in imaging protocol, abnormality detection by conventional breast thermography, is often a challenging task. Rotational thermography is a novel technique developed in order to overcome the limitations of conventional breast thermography. This paper evaluates this technique's potential for automatic detection of breast abnormality, from the perspective of cold challenge. Texture features are extracted in the spatial domain, from rotational thermogram series, prior to and post the application of cold challenge. These features are fed to a support vector machine for automatic classification of normal and malignant breasts, resulting in a classification accuracy of 83.3%. Feature reduction has been performed by principal component analysis. As a novel attempt, the ability of this technique to locate the abnormality has been studied. The results of the study indicate that rotational thermography holds great potential as a screening tool for breast cancer detection.

Land area change analysis following hurricane impacts in Delacroix, Louisiana, 2004--2009

USGS Publications Warehouse

Palaseanu-Lovejoy, Monica; Kranenburg, Christine J.; Brock, John C.

2012-01-01

The purpose of this project is to provide improved estimates of Louisiana wetland land loss due to hurricane impacts between 2004 and 2009 based upon a change detection mapping analysis that incorporates pre- and post-landfall (Hurricanes Katrina, Rita, Gustav, and Ike) fractional water classification of a combination of high resolution (QuickBird, IKONOS and Geoeye-1) and medium resolution (Landsat) satellite imagery. This second dataset focuses on Hurricanes Katrina and Gustav, which made landfall on August 29, 2005, and September 1, 2008, respectively. The study area is an approximately 1208-square-kilometer region surrounding Delacroix, Louisiana, in the eastern Delta Plain. Overall, 77 percent of the area remained unchanged between 2004 and 2009, and over 11 percent of the area was changed permanently by Hurricane Katrina (including both land gain and loss). Less than 3 percent was affected, either temporarily or permanently, by Hurricane Gustav. A related dataset (SIM 3141) focused on Hurricane Rita, which made landfall on the Louisiana/Texas border on September 24, 2005, as a Category 3 hurricane.

Bioavailability and biochemical effects of diclofenac sodium 0.1% ophthalmic solution in the domestic chicken (Gallus gallus domesticus).

PubMed

Griggs, Angela N; Yaw, Taylor J; Haynes, Joseph S; Ben-Shlomo, Gil; Tofflemire, Kyle L; Allbaugh, Rachel A

2017-03-01

To determine if topical ophthalmic diclofenac sodium 0.1% solution alters renal parameters in the domestic chicken, and to determine if the drug is detectable in plasma after topical ophthalmic administration. Thirty healthy domestic chickens. Over 7 days, six birds were treated unilaterally with one drop of artificial tear solution (group 1), 12 birds were treated unilaterally (group 2) and 12 bilaterally (group 3) with diclofenac sodium 0.1% ophthalmic solution. Treatments were provided every 12 h in all groups. Pre- and post-treatment plasma samples from all birds were evaluated for changes in albumin, total protein, and uric acid. Post-treatment samples of all birds, collected 15 min post-administration, were analyzed by high-performance liquid chromatography with mass spectrometry for diclofenac sodium detection. A randomly selected renal sample from each group was submitted for histopathologic review. Changes in pre- and post-treatment plasma albumin were significant (P < 0.05) in groups 2 and 3, but not for group 1. Pre- and post-treatment changes in total protein and uric acid were not significant for any group. Diclofenac sodium was not detectable (limit of detection = 0.10 ng/mL) in plasma samples from birds in group 1. Post-treatment concentration of diclofenac in group 3 was statistically greater than group 2 (P = 0.0008). Histopathologic changes did not identify diclofenac-induced acute renal tubular necrosis. Ophthalmic diclofenac sodium 0.1% administered topically every 12 h in one or both eyes for 7 days is detectable in systemic circulation in the domestic chicken, but does not cause overt significant changes in plasma uric acid or total protein. © 2016 American College of Veterinary Ophthalmologists.

Discovery and Classification in Astronomy

NASA Astrophysics Data System (ADS)

Dick, Steven J.

2012-01-01

Three decades after Martin Harwit's pioneering Cosmic Discovery (1981), and following on the recent IAU Symposium "Accelerating the Rate of Astronomical Discovery,” we have revisited the problem of discovery in astronomy, emphasizing new classes of objects. 82 such classes have been identified and analyzed, including 22 in the realm of the planets, 36 in the realm of the stars, and 24 in the realm of the galaxies. We find an extended structure of discovery, consisting of detection, interpretation and understanding, each with its own nuances and a microstructure including conceptual, technological and social roles. This is true with a remarkable degree of consistency over the last 400 years of telescopic astronomy, ranging from Galileo's discovery of satellites, planetary rings and star clusters, to the discovery of quasars and pulsars. Telescopes have served as "engines of discovery” in several ways, ranging from telescope size and sensitivity (planetary nebulae and spiral galaxies), to specialized detectors (TNOs) and the opening of the electromagnetic spectrum for astronomy (pulsars, pulsar planets, and most active galaxies). A few classes (radiation belts, the solar wind and cosmic rays), were initially discovered without the telescope. Classification also plays an important role in discovery. While it might seem that classification marks the end of discovery, or a post-discovery phase, in fact it often marks the beginning, even a pre-discovery phase. Nowhere is this more clearly seen than in the classification of stellar spectra, long before dwarfs, giants and supergiants were known, or their evolutionary sequence recognized. Classification may also be part of a post-discovery phase, as in the MK system of stellar classification, constructed after the discovery of stellar luminosity classes. Some classes are declared rather than discovered, as in the case of gas and ice giant planets, and, infamously, Pluto as a dwarf planet.

An analysis of stigma and suicide literacy in responses to suicides broadcast on social media.

PubMed

Li, Ang; Huang, Xiaoxiao; Jiao, Dongdong; O'Dea, Bridianne; Zhu, Tingshao; Christensen, Helen

2018-03-01

Broadcasting a suicide attempt on social media has become a public concern in China. Stigmatizing attitudes around such broadcast can limit help-seeking and increase the likelihood of death. To reduce stigmatizing attitudes, this paper aims to detect stigma expressions in social media posts through language use patterns and then identify suicide literacy in responses to such broadcast. Firstly, to examine linguistic patterns of stigma expressions, 6632 Weibo posts with keywords were collected and analyzed. Using 102 linguistic features, 2 classification models were built: one for differentiating between stigmatizing and nonstigmatizing attitudes, and one for differentiating between specific types of stigmatizing attitudes. Secondly, to identify the levels of suicide literacy, a content analysis was conducted on 4969 Weibo posts related to social media suicide. Firstly, the model accuracy ranged from 66.15% to 72.79%. Secondly, a total of 11.67% of the Weibo posts (n = 580) contained misinformation about suicide. In the category of knowledge of signs, 27.93% and 18.10% of posts endorsed the stigmatizing views that "suicide happens without warning" and "people who want to attempt suicide cannot change their mind quickly," both of which were related to a stigmatizing belief that a suicide attempt on social media is not genuine. In the category of knowledge of treatments, 35.17% of posts endorsed the stigmatizing view that "people who have thoughts about suicide should not tell others about it." This paper presents an opportunity for the dissemination of targeted online campaigns to increase mental health literacy and help-seeking. © 2018 John Wiley & Sons Australia, Ltd.

Aircraft MSS data registration and vegetation classification of wetland change detection

USGS Publications Warehouse

Christensen, E.J.; Jensen, J.R.; Ramsey, Elijah W.; Mackey, H.E.

1988-01-01

Portions of the Savannah River floodplain swamp were evaluated for vegetation change using high resolution (5a??6 m) aircraft multispectral scanner (MSS) data. Image distortion from aircraft movement prevented precise image-to-image registration in some areas. However, when small scenes were used (200-250 ha), a first-order linear transformation provided registration accuracies of less than or equal to one pixel. A larger area was registered using a piecewise linear method. Five major wetland classes were identified and evaluated for change. Phenological differences and the variable distribution of vegetation limited wetland type discrimination. Using unsupervised methods and ground-collected vegetation data, overall classification accuracies ranged from 84 per cent to 87 per cent for each scene. Results suggest that high-resolution aircraft MSS data can be precisely registered, if small areas are used, and that wetland vegetation change can be accurately detected and monitored.

Heart rate variability (HRV): an indicator of stress

NASA Astrophysics Data System (ADS)

Kaur, Balvinder; Durek, Joseph J.; O'Kane, Barbara L.; Tran, Nhien; Moses, Sophia; Luthra, Megha; Ikonomidou, Vasiliki N.

2014-05-01

Heart rate variability (HRV) can be an important indicator of several conditions that affect the autonomic nervous system, including traumatic brain injury, post-traumatic stress disorder and peripheral neuropathy [3], [4], [10] & [11]. Recent work has shown that some of the HRV features can potentially be used for distinguishing a subject's normal mental state from a stressed one [4], [13] & [14]. In all of these past works, although processing is done in both frequency and time domains, few classification algorithms have been explored for classifying normal from stressed RRintervals. In this paper we used 30 s intervals from the Electrocardiogram (ECG) time series collected during normal and stressed conditions, produced by means of a modified version of the Trier social stress test, to compute HRV-driven features and subsequently applied a set of classification algorithms to distinguish stressed from normal conditions. To classify RR-intervals, we explored classification algorithms that are commonly used for medical applications, namely 1) logistic regression (LR) [16] and 2) linear discriminant analysis (LDA) [6]. Classification performance for various levels of stress over the entire test was quantified using precision, accuracy, sensitivity and specificity measures. Results from both classifiers were then compared to find an optimal classifier and HRV features for stress detection. This work, performed under an IRB-approved protocol, not only provides a method for developing models and classifiers based on human data, but also provides a foundation for a stress indicator tool based on HRV. Further, these classification tools will not only benefit many civilian applications for detecting stress, but also security and military applications for screening such as: border patrol, stress detection for deception [3],[17], and wounded-warrior triage [12].

Remote Sensing Monitoring of Changes in Soil Salinity: A Case Study in Inner Mongolia, China.

PubMed

Wu, Jingwei; Vincent, Bernard; Yang, Jinzhong; Bouarfa, Sami; Vidal, Alain

2008-11-07

This study used archived remote sensing images to depict the history of changes in soil salinity in the Hetao Irrigation District in Inner Mongolia, China, with the purpose of linking these changes with land and water management practices and to draw lessons for salinity control. Most data came from LANDSAT satellite images taken in 1973, 1977, 1988, 1991, 1996, 2001, and 2006. In these years salt-affected areas were detected using a normal supervised classification method. Corresponding cropped areas were detected from NVDI (Normalized Difference Vegetation Index) values using an unsupervised method. Field samples and agricultural statistics were used to estimate the accuracy of the classification. Historical data concerning irrigation/drainage and the groundwater table were used to analyze the relation between changes in soil salinity and land and water management practices. Results showed that: (1) the overall accuracy of remote sensing in detecting soil salinity was 90.2%, and in detecting cropped area, 98%; (2) the installation/innovation of the drainage system did help to control salinity; and (3) a low ratio of cropped land helped control salinity in the Hetao Irrigation District. These findings suggest that remote sensing is a useful tool to detect soil salinity and has potential in evaluating and improving land and water management practices.

Documentation of procedures for textural/spatial pattern recognition techniques

NASA Technical Reports Server (NTRS)

Haralick, R. M.; Bryant, W. F.

1976-01-01

A C-130 aircraft was flown over the Sam Houston National Forest on March 21, 1973 at 10,000 feet altitude to collect multispectral scanner (MSS) data. Existing textural and spatial automatic processing techniques were used to classify the MSS imagery into specified timber categories. Several classification experiments were performed on this data using features selected from the spectral bands and a textural transform band. The results indicate that (1) spatial post-processing a classified image can cut the classification error to 1/2 or 1/3 of its initial value, (2) spatial post-processing the classified image using combined spectral and textural features produces a resulting image with less error than post-processing a classified image using only spectral features and (3) classification without spatial post processing using the combined spectral textural features tends to produce about the same error rate as a classification without spatial post processing using only spectral features.

Unsupervised Approaches for Post-Processing in Computationally Efficient Waveform-Similarity-Based Earthquake Detection

NASA Astrophysics Data System (ADS)

Bergen, K.; Yoon, C. E.; OReilly, O. J.; Beroza, G. C.

2015-12-01

Recent improvements in computational efficiency for waveform correlation-based detections achieved by new methods such as Fingerprint and Similarity Thresholding (FAST) promise to allow large-scale blind search for similar waveforms in long-duration continuous seismic data. Waveform similarity search applied to datasets of months to years of continuous seismic data will identify significantly more events than traditional detection methods. With the anticipated increase in number of detections and associated increase in false positives, manual inspection of the detection results will become infeasible. This motivates the need for new approaches to process the output of similarity-based detection. We explore data mining techniques for improved detection post-processing. We approach this by considering similarity-detector output as a sparse similarity graph with candidate events as vertices and similarities as weighted edges. Image processing techniques are leveraged to define candidate events and combine results individually processed at multiple stations. Clustering and graph analysis methods are used to identify groups of similar waveforms and assign a confidence score to candidate detections. Anomaly detection and classification are applied to waveform data for additional false detection removal. A comparison of methods will be presented and their performance will be demonstrated on a suspected induced and non-induced earthquake sequence.

Radiation-related caries assessment through the International Caries Detection and Assessment System and the Post-Radiation Dental Index.

PubMed

Palmier, Natalia Rangel; Ribeiro, Ana Carolina Prado; Fonsêca, Jéssica Montenegro; Salvajoli, João Victor; Vargas, Pablo Agustin; Lopes, Marcio Ajudarte; Brandão, Thaís Bianca; Santos-Silva, Alan Roger

2017-12-01

Although radiation-related caries (RRC) are a well-known toxicity of head and neck radiotherapy, a clinical classification system for RRC has not yet been clinically validated. Therefore, the aim of this study was to assess whether the International Caries Detection and Assessment System (ICDAS) and the Post-Radiation Dental Index (PRDI) were viable methods for the assessment of RRC. Clinicopathologic data and intraoral digital photographs of 60 patients (833 teeth) affected by RRC were assessed and classified according to the ICDAS and PRDI criteria. A total of 814 (97.7%) teeth presented RRC lesions ranging from early stage to complete tooth destruction. Mean scores for the whole sample were 5 for ICDAS and 3 for PRDI, indicating that RRC were diagnosed predominately in late stages. ICDAS and PRDI criteria underestimate the clinical expressivity of RRC by not including the whole qualitative clinical spectrum of RRC, such as enamel cracks, delamination, dental crown amputation, surface color alterations, and atypical lesions topography (incisal/cuspal caries). ICDAS and PRDI may not be considered viable for the assessment of RRC. The development of a specific clinical classification system is urgently needed to help clinicians recognize the peculiar patterns of RRC, particularly in incipient cases. Copyright © 2017 Elsevier Inc. All rights reserved.

A new feature extraction method for signal classification applied to cord dorsum potentials detection

PubMed Central

Vidaurre, D.; Rodríguez, E. E.; Bielza, C.; Larrañaga, P.; Rudomin, P.

2012-01-01

In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods. PMID:22929924

A new feature extraction method for signal classification applied to cord dorsum potential detection.

PubMed

Vidaurre, D; Rodríguez, E E; Bielza, C; Larrañaga, P; Rudomin, P

2012-10-01

In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods.

Multi-scale 46-year remote sensing change detection of diamond mining and land cover in a conflict and post-conflict setting

USGS Publications Warehouse

Dewitt, Jessica D.; Chirico, Peter G.; Bergstresser, Sarah E.; Warner, Timothy A.

2017-01-01

The town of Tortiya was created in the rural northern region of Côte d′Ivoire in the late 1940s to house workers for a new diamond mine. Nearly three decades later, the closure of the industrial-scale diamond mine in 1975 did not diminish the importance of diamond profits to the region's economy, and resulted in the growth of artisanal and small-scale diamond mining (ASM) within the abandoned industrial-scale mining concession. In the early 2000s, the violent conflict that arose in Côte d′Ivoire highlighted the importance of ASM land use to the local economy, but also brought about international concerns that diamond profits were being used to fund the rebellion. In recent years, cashew plantations have expanded exponentially in the region, diversifying economic activity, but also creating the potential for conflict between diamond mining and agricultural land uses. As the government looks to address the future of Tortiya and this potential conflict, a detailed spatio-temporal understanding of the changes in these two land uses over time may assist in informing policymaking. Remotely sensed imagery presents an objective and detailed spatial record of land use/land cover (LULC), and change detection methods can provide quantitative insight regarding regional land cover trends. However, the vastly different scales of ASM and cashew orchards present a unique challenge to comprehensive understanding of land use change in the region. In this study, moderate-scale categories of LULC, including cashew orchards, uncultivated forest, urban space, mining/ bare, and mixed vegetation, were produced through supervised classification of Landsat multispectral imagery from 1984, 1991, 2000, 2007, and 2014. The fine-scale ASM land use was identified through manual interpretation of annually acquired high resolution satellite imagery. Corona imagery was also integrated into the study to extend the temporal duration of the remote sensing record back to the period of industrial-scale mining. These different-scale analyses were then integrated to create a record of 46 years of mining activity and land cover change in Tortiya. While similar in spatial extent, the mining/ bare class in the integrated analysis exhibits a substantially different spatial distribution than in the original classifications. This additional information regarding the locations of ASM activity in the Tortiya area is important from a policy and planning perspective. The results of this study also suggest that LULC classifications of Landsat imagery do not consistently capture areas of ASM in the Côte d′Ivoire landscape.

Earthquake Damage Assessment over Port-au-Prince (Haiti) by Fusing Optical and SAR Data

NASA Astrophysics Data System (ADS)

Romaniello, V.; Piscini, A.; Bignami, C.; Anniballe, R.; Pierdicca, N.; Stramondo, S.

2016-08-01

This work proposes methodologies aiming at evaluating the sensitivity of optical and SAR change features obtained from satellite images with respect to the damage grade. The proposed methods are derived from the literature ([1], [2], [3], [4]) and the main novelty concerns the estimation of these change features at object scale.The test case is the Mw 7.0 earthquake that hit Haiti on January 12, 2010.The analysis of change detection indicators is based on ground truth information collected during a post- earthquake survey. We have generated the damage map of Port-au-Prince by considering a set of polygons extracted from the open source Open Street Map geo- database. The resulting damage map was calculated in terms of collapse ratio [5].We selected some features having a good sensitivity with damage at object scale [6]: the Normalised Difference Index, the Kullback-Libler Divergence, the Mutual Information and the Intensity Correlation Difference.The Naive Bayes and the Support Vector Machine classifiers were used to evaluate the goodness of these features. The classification results demonstrate that the simultaneous use of several change features from EO observations can improve the damage estimation at object scale.

Fuzzy logic based on-line fault detection and classification in transmission line.

PubMed

Adhikari, Shuma; Sinha, Nidul; Dorendrajit, Thingam

2016-01-01

This study presents fuzzy logic based online fault detection and classification of transmission line using Programmable Automation and Control technology based National Instrument Compact Reconfigurable i/o (CRIO) devices. The LabVIEW software combined with CRIO can perform real time data acquisition of transmission line. When fault occurs in the system current waveforms are distorted due to transients and their pattern changes according to the type of fault in the system. The three phase alternating current, zero sequence and positive sequence current data generated by LabVIEW through CRIO-9067 are processed directly for relaying. The result shows that proposed technique is capable of right tripping action and classification of type of fault at high speed therefore can be employed in practical application.

Classification of Traffic Related Short Texts to Analyse Road Problems in Urban Areas

NASA Astrophysics Data System (ADS)

Saldana-Perez, A. M. M.; Moreno-Ibarra, M.; Tores-Ruiz, M.

2017-09-01

The Volunteer Geographic Information (VGI) can be used to understand the urban dynamics. In the classification of traffic related short texts to analyze road problems in urban areas, a VGI data analysis is done over a social media's publications, in order to classify traffic events at big cities that modify the movement of vehicles and people through the roads, such as car accidents, traffic and closures. The classification of traffic events described in short texts is done by applying a supervised machine learning algorithm. In the approach users are considered as sensors which describe their surroundings and provide their geographic position at the social network. The posts are treated by a text mining process and classified into five groups. Finally, the classified events are grouped in a data corpus and geo-visualized in the study area, to detect the places with more vehicular problems.

A Global Perspective on Drinking-Water and Sanitation Classification: An Evaluation of Census Content

PubMed Central

Yu, Weiyu; Wardrop, Nicola A.; Bain, Robert E. S.; Lin, Yanzhao; Zhang, Ce; Wright, Jim A.

2016-01-01

Following the recent expiry of the United Nations’ 2015 Millennium Development Goals (MDGs), new international development agenda covering 2030 water, sanitation and hygiene (WASH) targets have been proposed, which imply new demands on data sources for monitoring relevant progress. This study evaluates drinking-water and sanitation classification systems from national census questionnaire content, based upon the most recent international policy changes, to examine national population census’s ability to capture drinking-water and sanitation availability, safety, accessibility, and sustainability. In total, 247 censuses from 83 low income and lower-middle income countries were assessed using a scoring system, intended to assess harmonised water supply and sanitation classification systems for each census relative to the typology needed to monitor the proposed post-2015 indicators of WASH targets. The results signal a lack of international harmonisation and standardisation in census categorisation systems, especially concerning safety, accessibility, and sustainability of services in current census content. This suggests further refinements and harmonisation of future census content may be necessary to reflect ambitions for post-2015 monitoring. PMID:26986472

A Global Perspective on Drinking-Water and Sanitation Classification: An Evaluation of Census Content.

PubMed

Yu, Weiyu; Wardrop, Nicola A; Bain, Robert E S; Lin, Yanzhao; Zhang, Ce; Wright, Jim A

2016-01-01

Following the recent expiry of the United Nations' 2015 Millennium Development Goals (MDGs), new international development agenda covering 2030 water, sanitation and hygiene (WASH) targets have been proposed, which imply new demands on data sources for monitoring relevant progress. This study evaluates drinking-water and sanitation classification systems from national census questionnaire content, based upon the most recent international policy changes, to examine national population census's ability to capture drinking-water and sanitation availability, safety, accessibility, and sustainability. In total, 247 censuses from 83 low income and lower-middle income countries were assessed using a scoring system, intended to assess harmonised water supply and sanitation classification systems for each census relative to the typology needed to monitor the proposed post-2015 indicators of WASH targets. The results signal a lack of international harmonisation and standardisation in census categorisation systems, especially concerning safety, accessibility, and sustainability of services in current census content. This suggests further refinements and harmonisation of future census content may be necessary to reflect ambitions for post-2015 monitoring.

Mapping and detecting bark beetle-caused tree mortality in the western United States

NASA Astrophysics Data System (ADS)

Meddens, Arjan J. H.

Recently, insect outbreaks across North America have dramatically increased and the forest area affected by bark beetles is similar to that affected by fire. Remote sensing offers the potential to detect insect outbreaks with high accuracy. Chapter one involved detection of insect-caused tree mortality on the tree level for a 90km2 area in northcentral Colorado. Classes of interest included green trees, multiple stages of post-insect attack tree mortality including dead trees with red needles ("red-attack") and dead trees without needles ("gray-attack"), and non-forest. The results illustrated that classification of an image with a spatial resolution similar to the area of a tree crown outperformed that from finer and coarser resolution imagery for mapping tree mortality and non-forest classes. I also demonstrated that multispectral imagery could be used to separate multiple postoutbreak attack stages (i.e., red-attack and gray-attack) from other classes in the image. In Chapter 2, I compared and improved methods for detecting bark beetle-caused tree mortality using medium-resolution satellite data. I found that overall classification accuracy was similar between single-date and multi-date classification methods. I developed regression models to predict percent red attack within a 30-m grid cell and these models explained >75% of the variance using three Landsat spectral explanatory variables. Results of the final product showed that approximately 24% of the forest within the Landsat scene was comprised of tree mortality caused by bark beetles. In Chapter 3, I developed a gridded data set with 1-km2 resolution using aerial survey data and improved estimates of tree mortality across the western US and British Columbia. In the US, I also produced an upper estimate by forcing the mortality area to match that from high-resolution imagery in Idaho, Colorado, and New Mexico. Cumulative mortality area from all bark beetles was 5.46 Mha in British Columbia in 2001-2010 and 0.47-5.37 Mha (lower and upper estimate) in the western conterminous US during 1997-2010. Improved methods for detection and mapping of insect outbreak areas will lead to improved assessments of the effects of these forest disturbances on the economy, carbon cycle (and feedback to climate change), fuel loads, hydrology and forest ecology.

The molecular cues for the biological effects of ionizing radiation dose and post-irradiation time on human breast cancer SKBR3 cell line: A Raman spectroscopy study.

PubMed

Jafarzadeh, Naser; Mani-Varnosfaderani, Ahmad; Gilany, Kambiz; Eynali, Samira; Ghaznavi, Habib; Shakeri-Zadeh, Ali

2018-03-01

Radiotherapy is one of the main modalities of cancer treatment. The utility of Raman spectroscopy (RS) for detecting the distinct radiobiological responses in human cancer cells is currently under investigation. RS holds great promises to provide good opportunities for personalizing radiotherapy treatments. Here, we report the effects of the radiation dose and post-irradiation time on the molecular changes in the human breast cancer SKBR3 cells, using RS. The SKBR3 cells were irradiated by gamma radiation with different doses of 0, 1, 2, 4, and 6 Gy. The Raman signals were acquired 24 and 48 h after the gamma radiation. The collected Raman spectra were analyzed by different statistical methods such as principal component analysis, linear discriminant analysis, and genetic algorithm. A thorough analysis of the obtained Raman signals revealed that 2 Gy of gamma radiation induces remarkable molecular and structural changes in the SKBR3 cells. We found that the wavenumbers in the range of 1000-1400 cm -1 in Raman spectra are selective for discriminating between the effects of the different doses of irradiation. The results also revealed that longer post-irradiation time leads to the relaxation of the cells to their initial state. The molecular changes that occurred in the 2Gy samples were mostly reversible. On the other hand, the exposure to doses higher than 4Gy induced serious irreversible changes, mainly seen in 2700-2800 cm -1 in Raman spectra. The classification models developed in this study would help to predict the radiation-based molecular changes induced in the cancer cells by only using RS. Also, this designed framework may facilitate the process of biodosimetry. Copyright © 2018 Elsevier B.V. All rights reserved.

Object-Based Classification and Change Detection of Hokkaido, Japan

NASA Astrophysics Data System (ADS)

Park, J. G.; Harada, I.; Kwak, Y.

2016-06-01

Topography and geology are factors to characterize the distribution of natural vegetation. Topographic contour is particularly influential on the living conditions of plants such as soil moisture, sunlight, and windiness. Vegetation associations having similar characteristics are present in locations having similar topographic conditions unless natural disturbances such as landslides and forest fires or artificial disturbances such as deforestation and man-made plantation bring about changes in such conditions. We developed a vegetation map of Japan using an object-based segmentation approach with topographic information (elevation, slope, slope direction) that is closely related to the distribution of vegetation. The results found that the object-based classification is more effective to produce a vegetation map than the pixel-based classification.

High dimensional land cover inference using remotely sensed modis data

NASA Astrophysics Data System (ADS)

Glanz, Hunter S.

Image segmentation persists as a major statistical problem, with the volume and complexity of data expanding alongside new technologies. Land cover classification, one of the most studied problems in Remote Sensing, provides an important example of image segmentation whose needs transcend the choice of a particular classification method. That is, the challenges associated with land cover classification pervade the analysis process from data pre-processing to estimation of a final land cover map. Many of the same challenges also plague the task of land cover change detection. Multispectral, multitemporal data with inherent spatial relationships have hardly received adequate treatment due to the large size of the data and the presence of missing values. In this work we propose a novel, concerted application of methods which provide a unified way to estimate model parameters, impute missing data, reduce dimensionality, classify land cover, and detect land cover changes. This comprehensive analysis adopts a Bayesian approach which incorporates prior knowledge to improve the interpretability, efficiency, and versatility of land cover classification and change detection. We explore a parsimonious, parametric model that allows for a natural application of principal components analysis to isolate important spectral characteristics while preserving temporal information. Moreover, it allows us to impute missing data and estimate parameters via expectation-maximization (EM). A significant byproduct of our framework includes a suite of training data assessment tools. To classify land cover, we employ a spanning tree approximation to a lattice Potts prior to incorporate spatial relationships in a judicious way and more efficiently access the posterior distribution of pixel labels. We then achieve exact inference of the labels via the centroid estimator. To detect land cover changes, we develop a new EM algorithm based on the same parametric model. We perform simulation studies to validate our models and methods, and conduct an extensive continental scale case study using MODIS data. The results show that we successfully classify land cover and recover the spatial patterns present in large scale data. Application of our change point method to an area in the Amazon successfully identifies the progression of deforestation through portions of the region.

Multitemporal analysis of Landsat images to detect land use land cover changes for monitoring soil sealing in the Nola area (Naples, Italy)

NASA Astrophysics Data System (ADS)

De Giglio, Michaela; Allocca, Maria; Franci, Francesca

2016-10-01

Land Use Land Cover Changes (LULCC) data provide objective information to support environmental policy, urban planning purposes and sustainable land development. Understanding of past land use/cover practices and current landscape patterns is critical to assess the effects of LULCC on the Earth system. Within the framework of soil sealing in Italy, the present study aims to assess the LULCC of the Nola area (Naples metropolitan area, Italy), relating to a thirty year period from 1984 to 2015. The urban sprawl affects this area causing the impervious surface increase, the loss in rural areas and landscape fragmentation. Located near Vesuvio volcano and crossed by artificial filled rivers, the study area is subject to landslide, hydraulic and volcanic risks. Landsat time series has been processed by means of the supervised per-pixel classification in order to produce multitemporal Land Use Land Cover maps. Then, post-classification comparison approach has been applied to quantify the changes occurring between 1984 and 2015, also analyzing the intermediate variations in 1999, namely every fifteen years. The results confirm the urban sprawl. The increase of the built-up areas mainly causes the habitat fragmentation and the agricultural land conversion of the Nola area that is already damaged by unauthorized disposal of urban waste. Moreover, considering the local risk maps, it was verified that some of the new urban areas were built over known hazardous sites. In order to limit the soil sealing, urgent measures and sustainable urban planning are required.

High Spatial resolution remote sensing for salt marsh change detection on Fire Island National Seashore

NASA Astrophysics Data System (ADS)

Campbell, A.; Wang, Y.

2017-12-01

Salt marshes are under increasing pressure due to anthropogenic stressors including sea level rise, nutrient enrichment, herbivory and disturbances. Salt marsh losses risk the important ecosystem services they provide including biodiversity, water filtration, wave attenuation, and carbon sequestration. This study determines salt marsh change on Fire Island National Seashore, a barrier island along the south shore of Long Island, New York. Object-based image analysis was used to classifying Worldview-2, high resolution satellite, and topobathymetric LiDAR. The site was impacted by Hurricane Sandy in October of 2012 causing a breach in the Barrier Island and extensive overwash. In situ training data from vegetation plots were used to train the Random Forest classifier. The object-based Worldview-2 classification achieved an overall classification accuracy of 92.75. Salt marsh change for the study site was determined by comparing the 2015 classification with a 1997 classification. The study found a shift from high marsh to low marsh and a reduction in Phragmites on Fire Island. Vegetation losses were observed along the edge of the marsh and in the marsh interior. The analysis agreed with many of the trends found throughout the region including the reduction of high marsh and decline of salt marsh. The reduction in Phragmites could be due to the species shrinking niche between rising seas and dune vegetation on barrier islands. The complex management issues facing salt marsh across the United States including sea level rise and eutrophication necessitate very high resolution classification and change detection of salt marsh to inform management decisions such as restoration, salt marsh migration, and nutrient inputs.

Virtual Soil Monoliths: Blending Traditional and Web-Based Educational Approaches

ERIC Educational Resources Information Center

Krzic, Maja; Strivelli, Rachel A.; Holmes, Emma; Grand, Stephanie; Dyanatkar, Saeed; Lavkulich, Les M.; Crowley, Chris

2013-01-01

Since soil plays a crucial role in all aspects of global environmental change, it is essential that post-secondary institutions provide students with a strong foundation in soil science concepts including soil classification. The onset of information technology (IT) and web-based multimedia have opened new avenues to better incorporate…

Mapping decadal land cover changes in the woodlands of north eastern Namibia using the Landsat satellite archive (1975-2014)

NASA Astrophysics Data System (ADS)

Wingate, Vladimir; Phinn, Stuart; Kuhn, Nikolaus

2016-04-01

Woodland savannahs provide essential ecosystem functions and services to communities. On the African continent, they are widely utilized and converted to intensive land uses. This study investigates the land cover changes over 108,038 km2 in NE Namibia using multi-sensor Landsat imagery, at decadal intervals from 1975 to 2014, with a post-classification change detection method and supervised Regression Tree classifiers. We discuss likely impacts of land tenure and reforms over the past four decades on changes in land use and land cover. These included losses, gains and exchanges between predominant land cover classes. Exchanges comprised logical conversions between woodland and agricultural classes, implying woodland clearing for arable farming, cropland abandonment and vegetation succession. The dominant change was a reduction in the area of the woodland class due to the expansion of the agricultural class, specifically, small-scale cereal and pastoral production. Woodland area decreased from 90% of the study area in 1975 to 83% in 2014, while cleared land increased from 9% to 14%. We found that the main land cover changes are conversion from woodland to agricultural and urban land uses, driven by urban expansion and woodland clearing for subsistence-based agriculture and pastoralism.

Post-operative rotator cuff integrity, based on Sugaya's classification, can reflect abduction muscle strength of the shoulder.

PubMed

Yoshida, Masahito; Collin, Phillipe; Josseaume, Thierry; Lädermann, Alexandre; Goto, Hideyuki; Sugimoto, Katumasa; Otsuka, Takanobu

2018-01-01

Magnetic resonance (MR) imaging is common in structural and qualitative assessment of the rotator cuff post-operatively. Rotator cuff integrity has been thought to be associated with clinical outcome. The purpose of this study was to evaluate the inter-observer reliability of cuff integrity (Sugaya's classification) and assess the correlation between Sugaya's classification and the clinical outcome. It was hypothesized that Sugaya's classification would show good reliability and good correlation with the clinical outcome. Post-operative MR images were taken two years post-operatively, following arthroscopic rotator cuff repair. For assessment of inter-rater reliability, all radiographic evaluations for the supraspinatus muscle were done by two orthopaedic surgeons and one radiologist. Rotator cuff integrity was classified into five categories, according to Sugaya's classification. Fatty infiltration was graded into four categories, based on the Fuchs' classification grading system. Muscle hypotrophy was graded as four grades, according to the scale proposed by Warner. The clinical outcome was assessed according to the constant scoring system pre-operatively and 2 years post-operatively. Of the sixty-two consecutive patients with full-thickness rotator cuff tears, fifty-two patients were reviewed in this study. These subjects included twenty-three men and twenty-nine women, with an average age of fifty-seven years. In terms of the inter-rater reliability between orthopaedic surgeons, Sugaya's classification showed the highest agreement [ICC (2.1) = 0.82] for rotator cuff integrity. The grade of fatty infiltration and muscle atrophy demonstrated good agreement, respectively (0.722 and 0.758). With regard to the inter-rater reliability between orthopaedic surgeon and radiologist, Sugaya's classification showed good reliability [ICC (2.1) = 0.70]. On the other hand, fatty infiltration and muscle hypotrophy classifications demonstrated fair and moderate agreement [ICC (2.1) = 0.39 and 0.49]. Although no significant correlation was found between overall post-operative constant score and Sugaya's classification, Sugaya's classification indicated significant correlation with the muscle strength score. Sugaya's classification showed repeatability and good agreement between the orthopaedist and radiologist, who are involved in the patient care for the rotator cuff tear. Common classification of rotator cuff integrity with good reliability will give appropriate information for clinicians to improve the patient care of the rotator cuff tear. This classification also would be helpful to predict the strength of arm abduction in the scapular plane. IV.

Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications

NASA Astrophysics Data System (ADS)

Zhu, Zhe

2017-08-01

The free and open access to all archived Landsat images in 2008 has completely changed the way of using Landsat data. Many novel change detection algorithms based on Landsat time series have been developed We present a comprehensive review of four important aspects of change detection studies based on Landsat time series, including frequencies, preprocessing, algorithms, and applications. We observed the trend that the more recent the study, the higher the frequency of Landsat time series used. We reviewed a series of image preprocessing steps, including atmospheric correction, cloud and cloud shadow detection, and composite/fusion/metrics techniques. We divided all change detection algorithms into six categories, including thresholding, differencing, segmentation, trajectory classification, statistical boundary, and regression. Within each category, six major characteristics of different algorithms, such as frequency, change index, univariate/multivariate, online/offline, abrupt/gradual change, and sub-pixel/pixel/spatial were analyzed. Moreover, some of the widely-used change detection algorithms were also discussed. Finally, we reviewed different change detection applications by dividing these applications into two categories, change target and change agent detection.

Automatic detection of snow avalanches in continuous seismic data using hidden Markov models

NASA Astrophysics Data System (ADS)

Heck, Matthias; Hammer, Conny; van Herwijnen, Alec; Schweizer, Jürg; Fäh, Donat

2018-01-01

Snow avalanches generate seismic signals as many other mass movements. Detection of avalanches by seismic monitoring is highly relevant to assess avalanche danger. In contrast to other seismic events, signals generated by avalanches do not have a characteristic first arrival nor is it possible to detect different wave phases. In addition, the moving source character of avalanches increases the intricacy of the signals. Although it is possible to visually detect seismic signals produced by avalanches, reliable automatic detection methods for all types of avalanches do not exist yet. We therefore evaluate whether hidden Markov models (HMMs) are suitable for the automatic detection of avalanches in continuous seismic data. We analyzed data recorded during the winter season 2010 by a seismic array deployed in an avalanche starting zone above Davos, Switzerland. We re-evaluated a reference catalogue containing 385 events by grouping the events in seven probability classes. Since most of the data consist of noise, we first applied a simple amplitude threshold to reduce the amount of data. As first classification results were unsatisfying, we analyzed the temporal behavior of the seismic signals for the whole data set and found that there is a high variability in the seismic signals. We therefore applied further post-processing steps to reduce the number of false alarms by defining a minimal duration for the detected event, implementing a voting-based approach and analyzing the coherence of the detected events. We obtained the best classification results for events detected by at least five sensors and with a minimal duration of 12 s. These processing steps allowed identifying two periods of high avalanche activity, suggesting that HMMs are suitable for the automatic detection of avalanches in seismic data. However, our results also showed that more sensitive sensors and more appropriate sensor locations are needed to improve the signal-to-noise ratio of the signals and therefore the classification.

Monitoring of peri-distal gastrectomy carbohydrate antigen 19-9 level in gastric juice and its significance

PubMed Central

Xu, A-Man; Huang, Lei; Han, Wen-Xiu; Wei, Zhi-Jian

2014-01-01

Gastric carcinoma is one of the most common and deadly malignancies nowadays, and carbohydrate antigen 19-9 (CA 19-9) in gastric juice has been rarely studied. To compare peri-distal gastrectomy (DG) gastric juice and serum CA 19-9 and reveal its significance, we selected 67 patients diagnosed with gastric carcinoma who underwent DG, and collected their perioperative gastric juice whose CA 19-9 was detected, with serum CA 19-9 monitored as a comparison. We found that: gastric juice CA 19-9 pre-gastrectomy was significantly correlated with tumor TNM classification, regarding tumor size, level of gastric wall invaded, differentiated grade and number of metastatic lymph nodes as influencing factors, while serum CA 19-9 revealed little information; gastric juice CA 19-9 was significantly correlated with radical degree, and regarded number of resected lymph nodes and classification of cutting edge as impact factors; thirteen patients whose gastric juice CA 19-9 rose post-DG showed features indicating poor prognosis; the difference of gastric juice CA 19-9 between pre- and post-gastrectomy was correlated with tumor TNM classification and radical degree, and regarded tumor size, number of resected metastatic and normal lymph nodes, sum of distances from tumor to cutting edges and classification of cutting edge as influential factors. We conclude that peri-DG gastric juice CA 19-9 reveals much information about tumor and radical gastrectomy, and may indicate prognosis; while serum CA 19-9 has limited significance. PMID:24482710

Quantifying the association between white matter integrity changes and subconcussive head impact exposure from a single season of youth and high school football using 3D convolutional neural networks

NASA Astrophysics Data System (ADS)

Saghafi, Behrouz; Murugesan, Gowtham; Davenport, Elizabeth; Wagner, Ben; Urban, Jillian; Kelley, Mireille; Jones, Derek; Powers, Alexander; Whitlow, Christopher; Stitzel, Joel; Maldjian, Joseph; Montillo, Albert

2018-02-01

The effect of subconcussive head impact exposure during contact sports, including American football, on brain health is poorly understood particularly in young and adolescent players, who may be more vulnerable to brain injury during periods of rapid brain maturation. This study aims to quantify the association between cumulative effects of head impact exposure from a single season of football on white matter (WM) integrity as measured with diffusion MRI. The study targets football players aged 9-18 years old. All players were imaged pre- and post-season with structural MRI and diffusion tensor MRI (DTI). Fractional Anisotropy (FA) maps, shown to be closely correlated with WM integrity, were computed for each subject, co-registered and subtracted to compute the change in FA per subject. Biomechanical metrics were collected at every practice and game using helmet mounted accelerometers. Each head impact was converted into a risk of concussion, and the risk of concussion-weighted cumulative exposure (RWE) was computed for each player for the season. Athletes with high and low RWE were selected for a two-category classification task. This task was addressed by developing a 3D Convolutional Neural Network (CNN) to automatically classify players into high and low impact exposure groups from the change in FA maps. Using the proposed model, high classification performance, including ROC Area Under Curve score of 85.71% and F1 score of 83.33% was achieved. This work adds to the growing body of evidence for the presence of detectable neuroimaging brain changes in white matter integrity from a single season of contact sports play, even in the absence of a clinically diagnosed concussion.

Chelation therapy to treat atherosclerosis, particularly in diabetes: Is it time to reconsider?

PubMed Central

Lamas, Gervasio A; Ergui, Ian

2016-01-01

Summary Reports and case series have suggested a possible beneficial effect of chelation therapy in patients with atherosclerotic disease. Small randomized trials conducted in patients with angina or peripheral artery disease, however, were not sufficiently powered to provide conclusive evidence on clinical outcomes. The Trial to Assess Chelation Therapy (TACT) was the first randomized trial adequately powered to detect the effects of chelation therapy on clinical endpoints. Chelation reduced adverse cardiovascular events in a post myocardial infarction (MI) population. Patients with diabetes demonstrated even greater benefit, with a number needed to treat of 6.5 patients to prevent a cardiac event over 5 years. These results led to the revision of the ACC/AHA guideline recommendations for chelation therapy, changing its classification from class III to class IIb. TACT2, a replicative trial, will assess the effects of chelation therapy on cardiovascular outcomes in diabetic patients with a prior myocardial infarction. PMID:27149141

Classification of Electrophotonic Images of Yogic Practice of Mudra through Neural Networks.

PubMed

Kumar, Kotikalapudi Shiva; Srinivasan, T M; Ilavarasu, Judu; Mondal, Biplob; Nagendra, H R

2018-01-01

Mudras signify a gesture with hands, eyes, and the body. Different configurations of the joining of fingertips are also termed mudra and are used by yoga practitioners for energy manipulation and for therapeutic applications. Electrophotonic imaging (EPI) captures the coronal discharge around the fingers as a result of electron capture from the ten fingers. The coronal discharge around each fingertip is studied to understand the effect of mudra on EPI parameters. The participants were from Swami Vivekananda Yoga Anusandhana Samsthana and Sushrutha Ayurvedic Medical College, in Bengaluru, India. There were 29 volunteers in the mudra group and 32 in the control group. There were two designs: one was a pre-post design with control the other was pre-post with repeated measures with 18 individuals practicing mudra for 3 days. The duration of intervention for the pre-post design was 10 min on the 1 st day, 15 min on the 2 nd day, and 20 min on the 3 rd day. A neural network classifier was used for classifying mudra and control samples. The EPI parameters, normalized area and average intensity, passed the test of normality Shapiro-Wilk. The Cohen's d , effect size was 0.988 and 0.974 for the mudra and control groups, respectively. Neural network-based analysis showed the classification accuracy of the post-intervention samples for mudra and control varied from 85% to 100% while the classification accuracy varied from 55% to 70% for the pre-intervention samples. The result of the mudra intervention showed statistically significant changes in the mean values on the 3 rd day compared to the 1 st day. The effect size of the variations in mudra was more than that of the control group. Mudra practice of a longer duration showed statistically significant change in the EPI parameter, average intensity in comparison to the practice on the 1 st day.

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

PubMed

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

2012-11-01

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

Multi-walled carbon nanotube-induced gene signatures in the mouse lung: potential predictive value for human lung cancer risk and prognosis

PubMed Central

Guo, Nancy L; Wan, Ying-Wooi; Denvir, James; Porter, Dale W; Pacurari, Maricica; Wolfarth, Michael G; Castranova, Vincent; Qian, Yong

2012-01-01

Concerns over the potential for multi-walled carbon nanotubes (MWCNT) to induce lung carcinogenesis have emerged. This study sought to (1) identify gene expression signatures in the mouse lungs following pharyngeal aspiration of well-dispersed MWCNT and (2) determine if these genes were associated with human lung cancer risk and progression. Genome-wide mRNA expression profiles were analyzed in mouse lungs (n=160) exposed to 0, 10, 20, 40, or 80 µg of MWCNT by pharyngeal aspiration at 1, 7, 28, and 56 days post-exposure. By using pairwise-Statistical Analysis of Microarray (SAM) and linear modeling, 24 genes were selected, which have significant changes in at least two time points, have a more than 1.5 fold change at all doses, and are significant in the linear model for the dose or the interaction of time and dose. Additionally, a 38-gene set was identified as related to cancer from 330 genes differentially expressed at day 56 post-exposure in functional pathway analysis. Using the expression profiles of the cancer-related gene set in 8 mice at day 56 post-exposure to 10 µg of MWCNT, a nearest centroid classification accurately predicts human lung cancer survival with a significant hazard ratio in training set (n=256) and test set (n=186). Furthermore, both gene signatures were associated with human lung cancer risk (n=164) with significant odds ratios. These results may lead to development of a surveillance approach for early detection of lung cancer and prognosis associated with MWCNT in the workplace. PMID:22891886

Pediatric Surgeon-Directed Wound Classification Improves Accuracy

PubMed Central

Zens, Tiffany J.; Rusy, Deborah A.; Gosain, Ankush

2015-01-01

Background Surgical wound classification (SWC) communicates the degree of contamination in the surgical field and is used to stratify risk of surgical site infection and compare outcomes amongst centers. We hypothesized that changing from nurse-directed to surgeon-directed SWC during a structured operative debrief we will improve accuracy of documentation. Methods An IRB-approved retrospective chart review was performed. Two time periods were defined: initially, SWC was determined and recorded by the circulating nurse (Pre-Debrief 6/2012-5/2013) and allowing six months for adoption and education, we implemented a structured operative debriefing including surgeon-directed SWC (Post-Debrief 1/2014-8/2014). Accuracy of SWC was determined for four commonly performed Pediatric General Surgery operations: inguinal hernia repair (clean), gastrostomy +/− Nissen fundoplication (clean-contaminated), appendectomy without perforation (contaminated), and appendectomy with perforation (dirty). Results 183 cases Pre-Debrief and 142 cases Post-Debrief met inclusion criteria. No differences between time periods were noted in regards to patient demographics, ASA class, or case mix. Accuracy of wound classification improved Post-Debrief (42% vs. 58.5%, p=0.003). Pre-Debrief, 26.8% of cases were overestimated or underestimated by more than one wound class, vs. 3.5% of cases Post-Debrief (p<0.001). Interestingly, the majority of Post-Debrief contaminated cases were incorrectly classified as clean-contaminated. Conclusions Implementation of a structured operative debrief including surgeon-directed SWC improves the percentage of correctly classified wounds and decreases the degree of inaccuracy in incorrectly classified cases. However, following implementation of the debriefing, we still observed a 41.5% rate of incorrect documentation, most notably in contaminated cases, indicating further education and process improvement is needed. PMID:27020829

Differentiation of pre-ablation and post-ablation late gadolinium-enhanced cardiac MRI scans of longstanding persistent atrial fibrillation patients

NASA Astrophysics Data System (ADS)

Yang, Guang; Zhuang, Xiahai; Khan, Habib; Haldar, Shouvik; Nyktari, Eva; Li, Lei; Ye, Xujiong; Slabaugh, Greg; Wong, Tom; Mohiaddin, Raad; Keegan, Jennifer; Firmin, David

2017-03-01

Late Gadolinium-Enhanced Cardiac MRI (LGE CMRI) is an emerging non-invasive technique to image and quantify preablation native and post-ablation atrial scarring. Previous studies have reported that enhanced image intensities of the atrial scarring in the LGE CMRI inversely correlate with the left atrial endocardial voltage invasively obtained by electro-anatomical mapping. However, the reported reproducibility of using LGE CMRI to identify and quantify atrial scarring is variable. This may be due to two reasons: first, delineation of the left atrium (LA) and pulmonary veins (PVs) anatomy generally relies on manual operation that is highly subjective, and this could substantially affect the subsequent atrial scarring segmentation; second, simple intensity based image features may not be good enough to detect subtle changes in atrial scarring. In this study, we hypothesized that texture analysis can provide reliable image features for the LGE CMRI images subject to accurate and objective delineation of the heart anatomy based on a fully-automated whole heart segmentation (WHS) method. We tested the extracted texture features to differentiate between pre-ablation and post-ablation LGE CMRI studies in longstanding persistent atrial fibrillation patients. These patients often have extensive native scarring and differentiation from post-ablation scarring can be difficult. Quantification results showed that our method is capable of solving this classification task, and we can envisage further deployment of this texture analysis based method for other clinical problems using LGE CMRI.

A Hessian-based methodology for automatic surface crack detection and classification from pavement images

NASA Astrophysics Data System (ADS)

Ghanta, Sindhu; Shahini Shamsabadi, Salar; Dy, Jennifer; Wang, Ming; Birken, Ralf

2015-04-01

Around 3,000,000 million vehicle miles are annually traveled utilizing the US transportation systems alone. In addition to the road traffic safety, maintaining the road infrastructure in a sound condition promotes a more productive and competitive economy. Due to the significant amounts of financial and human resources required to detect surface cracks by visual inspection, detection of these surface defects are often delayed resulting in deferred maintenance operations. This paper introduces an automatic system for acquisition, detection, classification, and evaluation of pavement surface cracks by unsupervised analysis of images collected from a camera mounted on the rear of a moving vehicle. A Hessian-based multi-scale filter has been utilized to detect ridges in these images at various scales. Post-processing on the extracted features has been implemented to produce statistics of length, width, and area covered by cracks, which are crucial for roadway agencies to assess pavement quality. This process has been realized on three sets of roads with different pavement conditions in the city of Brockton, MA. A ground truth dataset labeled manually is made available to evaluate this algorithm and results rendered more than 90% segmentation accuracy demonstrating the feasibility of employing this approach at a larger scale.

Comparison of six fire severity classification methods using Montana and Washington wildland fires

Treesearch

Pamela G. Sikkink

2015-01-01

Fire severity classifications are used in the post-fire environment to describe fire effects, such as soil alteration or fuel consumption, on the forest floor. Most of the developed classifications are limited because they address very specific fire effects or post-burn characteristics in the burned environment. However, because fire effects vary so much among soil,...

Monitoring strip mining and reclamation with LANDSAT data in Belmont County, Ohio

NASA Technical Reports Server (NTRS)

Witt, R. G.; Schaal, G. M.; Bly, B. G.

1983-01-01

The utility of LANDSAT digital data for mapping and monitoring surface mines in Belmont County, Ohio was investigated. Two data sets from 1976 and 1979 were processed to classify level 1 land covers and three strip mine categories in order to examine change over time and assess reclamation efforts. The two classifications were compared with aerial photographs. Results of the accuracy assessment show that both classifications are approximately 86 per cent correct, and that surface mine change detection (date-to-date comparison) is facilitated by the digital format of LANDSAT data.

Cases of Coastal Zone Change and Land Use/Land Cover Change: a learning module that goes beyond the "how" of doing image processing and change detection to asking the "why" about what are the "driving forces" of global change.

NASA Astrophysics Data System (ADS)

Ford, R. E.

2006-12-01

In 2006 the Loma Linda University ESSE21 Mesoamerican Project (Earth System Science Education for the 21st Century) along with partners such as the University of Redlands and California State University, Pomona, produced an online learning module that is designed to help students learn critical remote sensing skills-- specifically: ecosystem characterization, i.e. doing a supervised or unsupervised classification of satellite imagery in a tropical coastal environment. And, it would teach how to measure land use / land cover change (LULC) over time and then encourage students to use that data to assess the Human Dimensions of Global Change (HDGC). Specific objectives include: 1. Learn where to find remote sensing data and practice downloading, pre-processing, and "cleaning" the data for image analysis. 2. Use Leica-Geosystems ERDAS Imagine or IDRISI Kilimanjaro to analyze and display the data. 3. Do an unsupervised classification of a LANDSAT image of a protected area in Honduras, i.e. Cuero y Salado, Pico Bonito, or Isla del Tigre. 4. Virtually participate in a ground-validation exercise that would allow one to re-classify the image into a supervised classification using the FAO Global Land Cover Network (GLCN) classification system. 5. Learn more about each protected area's landscape, history, livelihood patterns and "sustainability" issues via virtual online tours that provide ground and space photos of different sites. This will help students in identifying potential "training sites" for doing a supervised classification. 6. Study other global, US, Canadian, and European land use/land cover classification systems and compare their advantages and disadvantages over the FAO/GLCN system. 7. Learn to appreciate the advantages and disadvantages of existing LULC classification schemes and adapt them to local-level user needs. 8. Carry out a change detection exercise that shows how land use and/or land cover has changed over time for the protected area of your choice. The presenter will demonstrate the module, assess the collaborative process which created it, and describe how it has been used so far by users in the US as well as in Honduras and elsewhere via a series joint workshops held in Mesoamerica. Suggestions for improvement will be requested. See the module and related content resources at: http://resweb.llu.edu/rford/ESSE21/LUCCModule/

Assessment of satellite and aircraft multispectral scanner data for strip-mine monitoring

NASA Technical Reports Server (NTRS)

Spisz, E. W.; Dooley, J. T.

1980-01-01

The application of LANDSAT multispectral scanner data to describe the mining and reclamation changes of a hilltop surface coal mine in the rugged, mountainous area of eastern Kentucky is presented. Original single band satellite imagery, computer enhanced single band imagery, and computer classified imagery are presented for four different data sets in order to demonstrate the land cover changes that can be detected. Data obtained with an 11 band multispectral scanner on board a C-47 aircraft at an altitude of 3000 meters are also presented. Comparing the satellite data with color, infrared aerial photography, and ground survey data shows that significant changes in the disrupted area can be detected from LANDSAT band 5 satellite imagery for mines with more than 100 acres of disturbed area. However, band-ratio (bands 5/6) imagery provides greater contrast than single band imagery and can provide a qualitative level 1 classification of the land cover that may be useful for monitoring either the disturbed mining area or the revegetation progress. However, if a quantitative, accurate classification of the barren or revegetated classes is required, it is necessary to perform a detailed, four band computer classification of the data.

Temporal stability and rates of post-depositional change in geochemical signatures of brown trout Salmo trutta scales.

PubMed

Ryan, D; Shephard, S; Kelly, F L

2016-09-01

This study investigates temporal stability in the scale microchemistry of brown trout Salmo trutta in feeder streams of a large heterogeneous lake catchment and rates of change after migration into the lake. Laser-ablation inductively coupled plasma mass spectrometry was used to quantify the elemental concentrations of Na, Mg, Mn, Cu, Zn, Ba and Sr in archived (1997-2002) scales of juvenile S. trutta collected from six major feeder streams of Lough Mask, County Mayo, Ireland. Water-element Ca ratios within these streams were determined for the fish sampling period and for a later period (2013-2015). Salmo trutta scale Sr and Ba concentrations were significantly (P < 0·05) correlated with stream water sample Sr:Ca and Ba:Ca ratios respectively from both periods, indicating multi-annual stability in scale and water-elemental signatures. Discriminant analysis of scale chemistries correctly classified 91% of sampled juvenile S. trutta to their stream of origin using a cross-validated classification model. This model was used to test whether assumed post-depositional change in scale element concentrations reduced correct natal stream classification of S. trutta in successive years after migration into Lough Mask. Fish residing in the lake for 1-3 years could be reliably classified to their most likely natal stream, but the probability of correct classification diminished strongly with longer lake residence. Use of scale chemistry to identify natal streams of lake S. trutta should focus on recent migrants, but may not require contemporary water chemistry data. © 2016 The Fisheries Society of the British Isles.

Identification of cataract and post-cataract surgery optical images using artificial intelligence techniques.

PubMed

Acharya, Rajendra Udyavara; Yu, Wenwei; Zhu, Kuanyi; Nayak, Jagadish; Lim, Teik-Cheng; Chan, Joey Yiptong

2010-08-01

Human eyes are most sophisticated organ, with perfect and interrelated subsystems such as retina, pupil, iris, cornea, lens and optic nerve. The eye disorder such as cataract is a major health problem in the old age. Cataract is formed by clouding of lens, which is painless and developed slowly over a long period. Cataract will slowly diminish the vision leading to the blindness. At an average age of 65, it is most common and one third of the people of this age in world have cataract in one or both the eyes. A system for detection of the cataract and to test for the efficacy of the post-cataract surgery using optical images is proposed using artificial intelligence techniques. Images processing and Fuzzy K-means clustering algorithm is applied on the raw optical images to detect the features specific to three classes to be classified. Then the backpropagation algorithm (BPA) was used for the classification. In this work, we have used 140 optical image belonging to the three classes. The ANN classifier showed an average rate of 93.3% in detecting normal, cataract and post cataract optical images. The system proposed exhibited 98% sensitivity and 100% specificity, which indicates that the results are clinically significant. This system can also be used to test the efficacy of the cataract operation by testing the post-cataract surgery optical images.

Combining TerraSAR-X and SPOT-5 data for object-based landslide detection

NASA Astrophysics Data System (ADS)

Friedl, B.; Hölbling, D.; Füreder, P.

2012-04-01

Landslide detection and classification is an essential requirement in pre- and post-disaster hazard analysis. In earlier studies landslide detection often was achieved through time-consuming and cost-intensive field surveys and visual orthophoto interpretation. Recent studies show that Earth Observation (EO) data offer new opportunities for fast, reliable and accurate landslide detection and classification, which may conduce to an effective landslide monitoring and landslide hazard management. To ensure the fast recognition and classification of landslides at a regional scale, a (semi-)automated object-based landslide detection approach is established for a study site situated in the Huaguoshan catchment, Southern Taiwan. The study site exhibits a high vulnerability to landslides and debris flows, which are predominantly typhoon-induced. Through the integration of optical satellite data (SPOT-5 with 2.5 m GSD), SAR (Synthetic Aperture Radar) data (TerraSAR-X Spotlight with 2.95 m GSD) and digital elevation information (DEM with 5 m GSD) including its derived products (e.g. slope, curvature, flow accumulation) landslides may be examined in a more efficient way as if relying on single data sources only. The combination of optical and SAR data in an object-based image analysis (OBIA) domain for landslide detection and classification has not been investigated so far, even if SAR imagery show valuable properties for landslide detection, which differ from optical data (e.g. high sensitivity to surface roughness and soil moisture). The main purpose of this study is to recognize and analyze existing landslides by applying object-based image analysis making use of eCognition software. OBIA provides a framework for examining features defined by spectral, spatial, textural, contextual as well as hierarchical properties. Objects are derived through image segmentation and serve as input for the classification process, which relies on transparent rulesets, representing knowledge. Through class modeling, an iterative process of segmentation and classification, objects can be addressed individually in a region-specific manner. The presented approach is marked by the comprehensive use of available data sets from various sources. This full integration of optical, SAR and DEM data conduces to the development of a robust method, which makes use of the most appropriate characteristics (e.g. spectral, textural, contextual) of each data set. The proposed method contributes to a more rapid and accurate landslide mapping in order to assist disaster and crisis management. Especially SAR data proves to be useful in the aftermath of an event, as radar sensors are mostly independent of illumination and weather conditions and therefore data is more likely to be available. The full data integration allows coming up with a robust approach for the detection and classification of landslides. However, more research is needed to make the best of the integration of SAR data in an object-based environment and for making the approach easier adaptable to different study sites and data.

Detection of Deforestation and Land Conversion in Rondonia, Brazil Using Change Detection Techniques

NASA Technical Reports Server (NTRS)

Guild, Liane S.; Cohen, Warren B,; Kauffman, J. Boone; Peterson, David L. (Technical Monitor)

2001-01-01

Fires associated with tropical deforestation, land conversion, and land use greatly contribute to emissions as well as the depletion of carbon and nutrient pools. The objective of this research was to compare change detection techniques for identifying deforestation and cattle pasture formation during a period of early colonization and agricultural expansion in the vicinity of Jamari, Rond6nia. Multi-date Landsat Thematic Mapper (TM) data between 1984 and 1992 was examined in a 94 370-ha area of active deforestation to map land cover change. The Tasseled Cap (TC) transformation was used to enhance the contrast between forest, cleared areas, and regrowth. TC images were stacked into a composite multi-date TC and used in a principal components (PC) transformation to identify change components. In addition, consecutive TC image pairs were differenced and stacked into a composite multi-date differenced image. A maximum likelihood classification of each image composite was compared for identification of land cover change. The multi-date TC composite classification had the best accuracy of 78.1% (kappa). By 1984, only 5% of the study area had been cleared, but by 1992, 11% of the area had been deforested, primarily for pasture and 7% lost due to hydroelectric dam flooding. Finally, discrimination of pasture versus cultivation was improved due to the ability to detect land under sustained clearing opened to land exhibiting regrowth with infrequent clearing.

Mapping Post-Fire Vegetation Recovery at Different Lithologies of Taygetos mt (greece) with Multi-Temporal Remote Sensing Data

NASA Astrophysics Data System (ADS)

Vassilakis, Emmanuel; Mallinis, George; Christopoulou, Anastasia; Farangitakis, Georgios-Pavlos; Papanikolaou, Ioannis; Arianoutsou, Margarita

2017-04-01

Mt Taygetos (2407m), located at southern Peloponnese (Greece) suffered a large fire during the summer of 2007. The fire burned approximately 45% of the area covered by the endemic Greek fir (Abies cephalonica) and Black Pine (Pinus nigra) forest ecosystems. The aim of the current study is to examine the potential differences on post-fire vegetation recovery imposed by the lithology as well as the geomorphology of the given area over sites of the same climatic and landscape conditions (elevation, aspect, slope etc.). The main lithologies consist of carbonate, permeable, not easily erodible formations (limestones and marbles) and clastic, impermeable (schists, slate and flysch) erodible ones. A time-series of high spatial resolution satellite images were interpreted, analyzed and compared in order to detect changes in vegetation coverage which could prioritize areas of interest for fieldwork campaigns. The remote sensing datasets were acquired before (Ikonos-2), a few months after (Quickbird-2) and some years after (Worldview-3) the 2007 fire. High resolution Digital Elevation Model was used for the ortho-rectification and co-registration of the remote sensing data, but also for the extraction of the mountainous landscape characteristics. The multi-temporal image dataset was analyzed through GEographic-Object Based Image Analysis (GEOBIA). Objects corresponding to different vegetation types through time were identified through spectral and textural features. The classification results were combined with basic layers such as lithological outcrops, pre-fire vegetation, landscape morphology etc., supplementing a spatial geodatabase used for classifying burnt areas with varying post-fire plant community recovery. We validated the results of the classification during fieldwork and found that at a local scale, where the landscape features are quite similar, the bedrock type proves to be an important factor for vegetation recovery, as it clearly defines the soil generation along with its properties. Plant species recovery seems to be controlled by the local lithology as it was found weaker in plots overlying limestones and marbles, comparing to that observed over schists, even for the same species. In conclusion, post-fire vegetation recovery seems to be a complex process controlled not only from species biology, but also from the geological features.

On-line Tool Wear Detection on DCMT070204 Carbide Tool Tip Based on Noise Cutting Audio Signal using Artificial Neural Network

NASA Astrophysics Data System (ADS)

Prasetyo, T.; Amar, S.; Arendra, A.; Zam Zami, M. K.

2018-01-01

This study develops an on-line detection system to predict the wear of DCMT070204 tool tip during the cutting process of the workpiece. The machine used in this research is CNC ProTurn 9000 to cut ST42 steel cylinder. The audio signal has been captured using the microphone placed in the tool post and recorded in Matlab. The signal is recorded at the sampling rate of 44.1 kHz, and the sampling size of 1024. The recorded signal is 110 data derived from the audio signal while cutting using a normal chisel and a worn chisel. And then perform signal feature extraction in the frequency domain using Fast Fourier Transform. Feature selection is done based on correlation analysis. And tool wear classification was performed using artificial neural networks with 33 input features selected. This artificial neural network is trained with back propagation method. Classification performance testing yields an accuracy of 74%.

A simulation study of scene confusion factors in sensing soil moisture from orbital radar

NASA Technical Reports Server (NTRS)

Ulaby, F. T. (Principal Investigator); Dobson, M. C.; Moezzi, S.; Roth, F. T.

1983-01-01

Simulated C-band radar imagery for a 124-km by 108-km test site in eastern Kansas is used to classify soil moisture. Simulated radar resolutions are 100 m by 100 m, 1 km by 1km, and 3 km by 3 km. Distributions of actual near-surface soil moisture are established daily for a 23-day accounting period using a water budget model. Within the 23-day period, three orbital radar overpasses are simulated roughly corresponding to generally moist, wet, and dry soil moisture conditions. The radar simulations are performed by a target/sensor interaction model dependent upon a terrain model, land-use classification, and near-surface soil moisture distribution. The accuracy of soil-moisture classification is evaluated for each single-date radar observation and also for multi-date detection of relative soil moisture change. In general, the results for single-date moisture detection show that 70% to 90% of cropland can be correctly classified to within +/- 20% of the true percent of field capacity. For a given radar resolution, the expected classification accuracy is shown to be dependent upon both the general soil moisture condition and also the geographical distribution of land-use and topographic relief. An analysis of cropland, urban, pasture/rangeland, and woodland subregions within the test site indicates that multi-temporal detection of relative soil moisture change is least sensitive to classification error resulting from scene complexity and topographic effects.

Post Anesthesia Care Unit Patient Classification System: The Direct Care Nursing Time Component

DTIC Science & Technology

1991-07-18

Removal 1304 Enema - Cleansing 1305 Enema - Retention 1306 Colostomy - Irrigation 1307 Colostomy - Dressing Change 1308 Lavage 1309 Paracentesis 1310...Curettage 1910 Vaginal /Pelvic Ex,.iiination 1911 Urinary Bladder Training 1912 Condom Catheter Application 1913 Peritoneal Dialysis - Initiation 1914...Hypothermia/Hyperthermia Treatment 2101 Oral Medication 2102 Intramuscular Medication 2103 Subcutaneous Medication 2104 Suppository, Rectal/ Vaginal

Comparison of whole-body post mortem 3D CT and autopsy evaluation in accidental blunt force traumatic death using the abbreviated injury scale classification.

PubMed

Daly, Barry; Abboud, Samir; Ali, Zabiullah; Sliker, Clint; Fowler, David

2013-02-10

Although 3D CT imaging data are available on survivors of accidental blunt trauma, little similar data has been collected and classified on major injuries in victims of fatal injuries. This study compared the sensitivity of post mortem computed tomography (PMCT) with that of conventional autopsy for major trauma findings classified according to the trauma Abbreviated Injury Scale (AIS). Whole-body 3D PMCT imaging data and full autopsy findings were analyzed on 21 victims of accidental blunt force trauma death. All major injuries were classified on the AIS scale with ratings from 3 (serious) to 6 (unsurvivable). Agreement between sensitivity of autopsy and PMCT for major injuries was determined. A total of 195 major injuries were detected (mean per fatality, 9.3; range, 1-14). Skeletal injuries by AIS grade included 37 grade 3, 45 grade 4, 12 grade 5, and 2 grade 6 major findings. Soft tissue injuries included 10 grade 3, 68 grade 4, 16 grade 5, and 5 grade 6 major findings. Of these, PMCT detected 165 (88 skeletal, 77 soft tissue), and autopsy detected 127 (59 skeletal, 68 soft tissue). PMCT agreed with autopsy in 86% and 76% of skeletal and soft tissue injuries, respectively. PMCT detected an additional 37 skeletal and 31 soft tissue injuries that were not identified at autopsy. Autopsy detected 8 skeletal and 22 soft tissue injuries that were not detected by PMCT. PMCT was more sensitive for skeletal (P=0.05) and head and neck region injury (P=0.043) detection. PMCT showed a trend for greater sensitivity than autopsy, but this did not reach statistical significance (P=0.083). 3D PMCT detected significantly more skeletal injuries than autopsy and a similar number of soft tissue injuries to autopsy and promises to be a sensitive tool for detection and classification of skeletal injuries in fatal blunt force accidental trauma. Use of the AIS scale allows standardized categorization and quantification of injuries that contribute to death in such cases and allows more objective comparison between autopsy and PMCT. Copyright © 2012. Published by Elsevier Ireland Ltd.

Change analysis in the United Arab Emirates: An investigation of techniques

USGS Publications Warehouse

Sohl, Terry L.

1999-01-01

Much of the landscape of the United Arab Emirates has been transformed over the past 15 years by massive afforestation, beautification, and agricultural programs. The "greening" of the United Arab Emirates has had environmental consequences, however, including degraded groundwater quality and possible damage to natural regional ecosystems. Personnel from the Ground- Water Research project, a joint effort between the National Drilling Company of the Abu Dhabi Emirate and the U.S. Geological Survey, were interested in studying landscape change in the Abu Dhabi Emirate using Landsat thematic mapper (TM) data. The EROs Data Center in Sioux Falls, South Dakota was asked to investigate land-cover change techniques that (1) provided locational, quantitative, and qualitative information on landcover change within the Abu Dhabi Emirate; and (2) could be easily implemented by project personnel who were relatively inexperienced in remote sensing. A number of products were created with 1987 and 1996 Landsat TM data using change-detection techniques, including univariate image differencing, an "enhanced" image differencing, vegetation index differencing, post-classification differencing, and changevector analysis. The different techniques provided products that varied in levels of adequacy according to the specific application and the ease of implementation and interpretation. Specific quantitative values of change were most accurately and easily provided by the enhanced image-differencing technique, while the change-vector analysis excelled at providing rich qualitative detail about the nature of a change. 

A habituation based approach for detection of visual changes in surveillance camera

NASA Astrophysics Data System (ADS)

Sha'abani, M. N. A. H.; Adan, N. F.; Sabani, M. S. M.; Abdullah, F.; Nadira, J. H. S.; Yasin, M. S. M.

2017-09-01

This paper investigates a habituation based approach in detecting visual changes using video surveillance systems in a passive environment. Various techniques have been introduced for dynamic environment such as motion detection, object classification and behaviour analysis. However, in a passive environment, most of the scenes recorded by the surveillance system are normal. Therefore, implementing a complex analysis all the time in the passive environment resulting on computationally expensive, especially when using a high video resolution. Thus, a mechanism of attention is required, where the system only responds to an abnormal event. This paper proposed a novelty detection mechanism in detecting visual changes and a habituation based approach to measure the level of novelty. The objective of the paper is to investigate the feasibility of the habituation based approach in detecting visual changes. Experiment results show that the approach are able to accurately detect the presence of novelty as deviations from the learned knowledge.

Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative

NASA Astrophysics Data System (ADS)

Zhu, Zhe; Gallant, Alisa L.; Woodcock, Curtis E.; Pengra, Bruce; Olofsson, Pontus; Loveland, Thomas R.; Jin, Suming; Dahal, Devendra; Yang, Limin; Auch, Roger F.

2016-12-01

The U.S. Geological Survey's Land Change Monitoring, Assessment, and Projection (LCMAP) initiative is a new end-to-end capability to continuously track and characterize changes in land cover, use, and condition to better support research and applications relevant to resource management and environmental change. Among the LCMAP product suite are annual land cover maps that will be available to the public. This paper describes an approach to optimize the selection of training and auxiliary data for deriving the thematic land cover maps based on all available clear observations from Landsats 4-8. Training data were selected from map products of the U.S. Geological Survey's Land Cover Trends project. The Random Forest classifier was applied for different classification scenarios based on the Continuous Change Detection and Classification (CCDC) algorithm. We found that extracting training data proportionally to the occurrence of land cover classes was superior to an equal distribution of training data per class, and suggest using a total of 20,000 training pixels to classify an area about the size of a Landsat scene. The problem of unbalanced training data was alleviated by extracting a minimum of 600 training pixels and a maximum of 8000 training pixels per class. We additionally explored removing outliers contained within the training data based on their spectral and spatial criteria, but observed no significant improvement in classification results. We also tested the importance of different types of auxiliary data that were available for the conterminous United States, including: (a) five variables used by the National Land Cover Database, (b) three variables from the cloud screening "Function of mask" (Fmask) statistics, and (c) two variables from the change detection results of CCDC. We found that auxiliary variables such as a Digital Elevation Model and its derivatives (aspect, position index, and slope), potential wetland index, water probability, snow probability, and cloud probability improved the accuracy of land cover classification. Compared to the original strategy of the CCDC algorithm (500 pixels per class), the use of the optimal strategy improved the classification accuracies substantially (15-percentage point increase in overall accuracy and 4-percentage point increase in minimum accuracy).

Detection of inter-patient left and right bundle branch block heartbeats in ECG using ensemble classifiers

PubMed Central

2014-01-01

Background Left bundle branch block (LBBB) and right bundle branch block (RBBB) not only mask electrocardiogram (ECG) changes that reflect diseases but also indicate important underlying pathology. The timely detection of LBBB and RBBB is critical in the treatment of cardiac diseases. Inter-patient heartbeat classification is based on independent training and testing sets to construct and evaluate a heartbeat classification system. Therefore, a heartbeat classification system with a high performance evaluation possesses a strong predictive capability for unknown data. The aim of this study was to propose a method for inter-patient classification of heartbeats to accurately detect LBBB and RBBB from the normal beat (NORM). Methods This study proposed a heartbeat classification method through a combination of three different types of classifiers: a minimum distance classifier constructed between NORM and LBBB; a weighted linear discriminant classifier between NORM and RBBB based on Bayesian decision making using posterior probabilities; and a linear support vector machine (SVM) between LBBB and RBBB. Each classifier was used with matching features to obtain better classification performance. The final types of the test heartbeats were determined using a majority voting strategy through the combination of class labels from the three classifiers. The optimal parameters for the classifiers were selected using cross-validation on the training set. The effects of different lead configurations on the classification results were assessed, and the performance of these three classifiers was compared for the detection of each pair of heartbeat types. Results The study results showed that a two-lead configuration exhibited better classification results compared with a single-lead configuration. The construction of a classifier with good performance between each pair of heartbeat types significantly improved the heartbeat classification performance. The results showed a sensitivity of 91.4% and a positive predictive value of 37.3% for LBBB and a sensitivity of 92.8% and a positive predictive value of 88.8% for RBBB. Conclusions A multi-classifier ensemble method was proposed based on inter-patient data and demonstrated a satisfactory classification performance. This approach has the potential for application in clinical practice to distinguish LBBB and RBBB from NORM of unknown patients. PMID:24903422

Detection of inter-patient left and right bundle branch block heartbeats in ECG using ensemble classifiers.

PubMed

Huang, Huifang; Liu, Jie; Zhu, Qiang; Wang, Ruiping; Hu, Guangshu

2014-06-05

Left bundle branch block (LBBB) and right bundle branch block (RBBB) not only mask electrocardiogram (ECG) changes that reflect diseases but also indicate important underlying pathology. The timely detection of LBBB and RBBB is critical in the treatment of cardiac diseases. Inter-patient heartbeat classification is based on independent training and testing sets to construct and evaluate a heartbeat classification system. Therefore, a heartbeat classification system with a high performance evaluation possesses a strong predictive capability for unknown data. The aim of this study was to propose a method for inter-patient classification of heartbeats to accurately detect LBBB and RBBB from the normal beat (NORM). This study proposed a heartbeat classification method through a combination of three different types of classifiers: a minimum distance classifier constructed between NORM and LBBB; a weighted linear discriminant classifier between NORM and RBBB based on Bayesian decision making using posterior probabilities; and a linear support vector machine (SVM) between LBBB and RBBB. Each classifier was used with matching features to obtain better classification performance. The final types of the test heartbeats were determined using a majority voting strategy through the combination of class labels from the three classifiers. The optimal parameters for the classifiers were selected using cross-validation on the training set. The effects of different lead configurations on the classification results were assessed, and the performance of these three classifiers was compared for the detection of each pair of heartbeat types. The study results showed that a two-lead configuration exhibited better classification results compared with a single-lead configuration. The construction of a classifier with good performance between each pair of heartbeat types significantly improved the heartbeat classification performance. The results showed a sensitivity of 91.4% and a positive predictive value of 37.3% for LBBB and a sensitivity of 92.8% and a positive predictive value of 88.8% for RBBB. A multi-classifier ensemble method was proposed based on inter-patient data and demonstrated a satisfactory classification performance. This approach has the potential for application in clinical practice to distinguish LBBB and RBBB from NORM of unknown patients.

A Dual-Process Account of Auditory Change Detection

ERIC Educational Resources Information Center

McAnally, Ken I.; Martin, Russell L.; Eramudugolla, Ranmalee; Stuart, Geoffrey W.; Irvine, Dexter R. F.; Mattingley, Jason B.

2010-01-01

Listeners can be "deaf" to a substantial change in a scene comprising multiple auditory objects unless their attention has been directed to the changed object. It is unclear whether auditory change detection relies on identification of the objects in pre- and post-change scenes. We compared the rates at which listeners correctly identify changed…

Change Detection Algorithms for Surveillance in Visual IoT: A Comparative Study

NASA Astrophysics Data System (ADS)

Akram, Beenish Ayesha; Zafar, Amna; Akbar, Ali Hammad; Wajid, Bilal; Chaudhry, Shafique Ahmad

2018-01-01

The VIoT (Visual Internet of Things) connects virtual information world with real world objects using sensors and pervasive computing. For video surveillance in VIoT, ChD (Change Detection) is a critical component. ChD algorithms identify regions of change in multiple images of the same scene recorded at different time intervals for video surveillance. This paper presents performance comparison of histogram thresholding and classification ChD algorithms using quantitative measures for video surveillance in VIoT based on salient features of datasets. The thresholding algorithms Otsu, Kapur, Rosin and classification methods k-means, EM (Expectation Maximization) were simulated in MATLAB using diverse datasets. For performance evaluation, the quantitative measures used include OSR (Overall Success Rate), YC (Yule's Coefficient) and JC (Jaccard's Coefficient), execution time and memory consumption. Experimental results showed that Kapur's algorithm performed better for both indoor and outdoor environments with illumination changes, shadowing and medium to fast moving objects. However, it reflected degraded performance for small object size with minor changes. Otsu algorithm showed better results for indoor environments with slow to medium changes and nomadic object mobility. k-means showed good results in indoor environment with small object size producing slow change, no shadowing and scarce illumination changes.

A Practical and Automated Approach to Large Area Forest Disturbance Mapping with Remote Sensing

PubMed Central

Ozdogan, Mutlu

2014-01-01

In this paper, I describe a set of procedures that automate forest disturbance mapping using a pair of Landsat images. The approach is built on the traditional pair-wise change detection method, but is designed to extract training data without user interaction and uses a robust classification algorithm capable of handling incorrectly labeled training data. The steps in this procedure include: i) creating masks for water, non-forested areas, clouds, and cloud shadows; ii) identifying training pixels whose value is above or below a threshold defined by the number of standard deviations from the mean value of the histograms generated from local windows in the short-wave infrared (SWIR) difference image; iii) filtering the original training data through a number of classification algorithms using an n-fold cross validation to eliminate mislabeled training samples; and finally, iv) mapping forest disturbance using a supervised classification algorithm. When applied to 17 Landsat footprints across the U.S. at five-year intervals between 1985 and 2010, the proposed approach produced forest disturbance maps with 80 to 95% overall accuracy, comparable to those obtained from traditional approaches to forest change detection. The primary sources of mis-classification errors included inaccurate identification of forests (errors of commission), issues related to the land/water mask, and clouds and cloud shadows missed during image screening. The approach requires images from the peak growing season, at least for the deciduous forest sites, and cannot readily distinguish forest harvest from natural disturbances or other types of land cover change. The accuracy of detecting forest disturbance diminishes with the number of years between the images that make up the image pair. Nevertheless, the relatively high accuracies, little or no user input needed for processing, speed of map production, and simplicity of the approach make the new method especially practical for forest cover change analysis over very large regions. PMID:24717283

A practical and automated approach to large area forest disturbance mapping with remote sensing.

PubMed

Ozdogan, Mutlu

2014-01-01

In this paper, I describe a set of procedures that automate forest disturbance mapping using a pair of Landsat images. The approach is built on the traditional pair-wise change detection method, but is designed to extract training data without user interaction and uses a robust classification algorithm capable of handling incorrectly labeled training data. The steps in this procedure include: i) creating masks for water, non-forested areas, clouds, and cloud shadows; ii) identifying training pixels whose value is above or below a threshold defined by the number of standard deviations from the mean value of the histograms generated from local windows in the short-wave infrared (SWIR) difference image; iii) filtering the original training data through a number of classification algorithms using an n-fold cross validation to eliminate mislabeled training samples; and finally, iv) mapping forest disturbance using a supervised classification algorithm. When applied to 17 Landsat footprints across the U.S. at five-year intervals between 1985 and 2010, the proposed approach produced forest disturbance maps with 80 to 95% overall accuracy, comparable to those obtained from traditional approaches to forest change detection. The primary sources of mis-classification errors included inaccurate identification of forests (errors of commission), issues related to the land/water mask, and clouds and cloud shadows missed during image screening. The approach requires images from the peak growing season, at least for the deciduous forest sites, and cannot readily distinguish forest harvest from natural disturbances or other types of land cover change. The accuracy of detecting forest disturbance diminishes with the number of years between the images that make up the image pair. Nevertheless, the relatively high accuracies, little or no user input needed for processing, speed of map production, and simplicity of the approach make the new method especially practical for forest cover change analysis over very large regions.

Pseudogynecomastia after massive weight loss: detectability of technique, patient satisfaction, and classification.

PubMed

Gusenoff, Jeffrey A; Coon, Devin; Rubin, J Peter

2008-11-01

An increasing number of male patients are presenting for treatment of male chest deformity after massive weight loss. The authors prefer to preserve the nipple-areola complex on a dermoglandular pedicle. They sought to identify detectability of technique, assess patient satisfaction, and outline a treatment algorithm for this population. Ten male massive weight loss patients underwent chest-contouring procedures over a period of 6 years and were surveyed to identify satisfaction with reconstruction. Preoperative photographs were used to devise a classification system. Twenty-seven medical professionals evaluated and rated digital photographs of the patients. Eight patients had pedicled reconstructions and two had free-nipple grafts. Mean age was 42.9 +/- 9.5 years, mean pre-weight loss body mass index was 54.1 +/- 10.6, post-weight loss body mass index was 29.4 +/- 4.5, and mean change in body mass index was 24.8 +/- 9.7. All patients would have surgery again, nine would recommend it to a friend, six would go shirtless in public, nine reported no loss of nipple sensation, and three reported dysesthesias of the nipple-areola complex. Medical professionals reproducibly associated poor wound healing with free-nipple grafting and rated poorly positioned nipple-areola complexes with low aesthetic scores. Medical professional scores for chest contour and nipple-areola complex aesthetics did not correlate with technique and were lower than scores provided by the patients. Patient satisfaction for treatment of the male chest deformity after massive weight loss is high. In carefully selected patients, preservation of the nipple-areola complex on a dermoglandular pedicle can aid in achieving an optimal aesthetic result.

VHR satellite multitemporal data to extract cultural landscape changes in the roman site of Grumentum

NASA Astrophysics Data System (ADS)

masini, nicola; Lasaponara, Rosa

2013-04-01

The papers deals with the use of VHR satellite multitemporal data set to extract cultural landscape changes in the roman site of Grumentum Grumentum is an ancient town, 50 km south of Potenza, located near the roman road of Via Herculea which connected the Venusia, in the north est of Basilicata, with Heraclea in the Ionian coast. The first settlement date back to the 6th century BC. It was resettled by the Romans in the 3rd century BC. Its urban fabric which evidences a long history from the Republican age to late Antiquity (III BC-V AD) is composed of the typical urban pattern of cardi and decumani. Its excavated ruins include a large amphitheatre, a theatre, the thermae, the Forum and some temples. There are many techniques nowadays available to capture and record differences in two or more images. In this paper we focus and apply the two main approaches which can be distinguished into : (i) unsupervised and (ii) supervised change detection methods. Unsupervised change detection methods are generally based on the transformation of the two multispectral images in to a single band or multiband image which are further analyzed to identify changes Unsupervised change detection techniques are generally based on three basic steps (i) the preprocessing step, (ii) a pixel-by-pixel comparison is performed, (iii). Identification of changes according to the magnitude an direction (positive /negative). Unsupervised change detection are generally based on the transformation of the two multispectral images into a single band or multiband image which are further analyzed to identify changes. Than the separation between changed and unchanged classes is obtained from the magnitude of the resulting spectral change vectors by means of empirical or theoretical well founded approaches Supervised change detection methods are generally based on supervised classification methods, which require the availability of a suitable training set for the learning process of the classifiers. Unsupervised change detection techniques are generally based on three basic steps (i) the preprocessing step, (ii) supervised classification is performed on the single dates or on the map obtained as the difference of two dates, (iii). Identification of changes according to the magnitude an direction (positive /negative). Supervised change detection are generally based on supervised classification methods, which require the availability of a suitable training set for the learning process of the classifiers, therefore these algorithms require a preliminary knowledge necessary: (i) to generate representative parameters for each class of interest; and (ii) to carry out the training stage Advantages and disadvantages of the supervised and unsupervised approaches are discuss. Finally results from the the satellite multitemporal dataset was also integrated with aerial photos from historical archive in order to expand the time window of the investigation and capture landscape changes occurred from the Agrarian Reform, in the 50s, up today.

North American Magazine Coverage of Skin Cancer and Recreational Tanning Before and After the WHO/IARC 2009 Classification of Indoor Tanning Devices as Carcinogenic.

PubMed

McWhirter, Jennifer E; Hoffman-Goetz, Laurie

2015-09-01

The mass media is an influential source of skin cancer information for the public. In 2009, the World Health Organization's International Agency for Research on Cancer classified UV radiation from tanning devices as carcinogenic. Our objective was to determine if media coverage of skin cancer and recreational tanning increased in volume or changed in nature after this classification. We conducted a directed content analysis on 29 North American popular magazines (2007-2012) to investigate the overall volume of articles on skin cancer and recreational tanning and, more specifically, the presence of skin cancer risk factors, UV behaviors, and early detection information in article text (n = 410) and images (n = 714). The volume of coverage on skin cancer and recreational tanning did not increase significantly after the 2009 classification of tanning beds as carcinogenic. Key-related messages, including that UV exposure is a risk factor for skin cancer and that indoor tanning should be avoided, were not reported more frequently after the classification, but the promotion of the tanned look as attractive was conveyed more often in images afterwards (p < .01). Content promoting high-SPF sunscreen use increased after the classification (p < .01), but there were no significant positive changes in the frequency of coverage of skin cancer risk factors, other UV behaviors, or early detection information over time. The classification of indoor tanning beds as carcinogenic had no significant impact on the volume or nature of skin cancer and recreational tanning coverage in magazines.

Anomalous change detection in imagery

DOEpatents

Theiler, James P [Los Alamos, NM; Perkins, Simon J [Santa Fe, NM

2011-05-31

A distribution-based anomaly detection platform is described that identifies a non-flat background that is specified in terms of the distribution of the data. A resampling approach is also disclosed employing scrambled resampling of the original data with one class specified by the data and the other by the explicit distribution, and solving using binary classification.

Selecting a Classification Ensemble and Detecting Process Drift in an Evolving Data Stream

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

Heredia-Langner, Alejandro; Rodriguez, Luke R.; Lin, Andy

2015-09-30

We characterize the commercial behavior of a group of companies in a common line of business using a small ensemble of classifiers on a stream of records containing commercial activity information. This approach is able to effectively find a subset of classifiers that can be used to predict company labels with reasonable accuracy. Performance of the ensemble, its error rate under stable conditions, can be characterized using an exponentially weighted moving average (EWMA) statistic. The behavior of the EWMA statistic can be used to monitor a record stream from the commercial network and determine when significant changes have occurred. Resultsmore » indicate that larger classification ensembles may not necessarily be optimal, pointing to the need to search the combinatorial classifier space in a systematic way. Results also show that current and past performance of an ensemble can be used to detect when statistically significant changes in the activity of the network have occurred. The dataset used in this work contains tens of thousands of high level commercial activity records with continuous and categorical variables and hundreds of labels, making classification challenging.« less

Identifying Key Hospital Service Quality Factors in Online Health Communities

PubMed Central

Jung, Yuchul; Hur, Cinyoung; Jung, Dain

2015-01-01

Background The volume of health-related user-created content, especially hospital-related questions and answers in online health communities, has rapidly increased. Patients and caregivers participate in online community activities to share their experiences, exchange information, and ask about recommended or discredited hospitals. However, there is little research on how to identify hospital service quality automatically from the online communities. In the past, in-depth analysis of hospitals has used random sampling surveys. However, such surveys are becoming impractical owing to the rapidly increasing volume of online data and the diverse analysis requirements of related stakeholders. Objective As a solution for utilizing large-scale health-related information, we propose a novel approach to identify hospital service quality factors and overtime trends automatically from online health communities, especially hospital-related questions and answers. Methods We defined social media–based key quality factors for hospitals. In addition, we developed text mining techniques to detect such factors that frequently occur in online health communities. After detecting these factors that represent qualitative aspects of hospitals, we applied a sentiment analysis to recognize the types of recommendations in messages posted within online health communities. Korea’s two biggest online portals were used to test the effectiveness of detection of social media–based key quality factors for hospitals. Results To evaluate the proposed text mining techniques, we performed manual evaluations on the extraction and classification results, such as hospital name, service quality factors, and recommendation types using a random sample of messages (ie, 5.44% (9450/173,748) of the total messages). Service quality factor detection and hospital name extraction achieved average F1 scores of 91% and 78%, respectively. In terms of recommendation classification, performance (ie, precision) is 78% on average. Extraction and classification performance still has room for improvement, but the extraction results are applicable to more detailed analysis. Further analysis of the extracted information reveals that there are differences in the details of social media–based key quality factors for hospitals according to the regions in Korea, and the patterns of change seem to accurately reflect social events (eg, influenza epidemics). Conclusions These findings could be used to provide timely information to caregivers, hospital officials, and medical officials for health care policies. PMID:25855612

Sequential structural damage diagnosis algorithm using a change point detection method

NASA Astrophysics Data System (ADS)

Noh, H.; Rajagopal, R.; Kiremidjian, A. S.

2013-11-01

This paper introduces a damage diagnosis algorithm for civil structures that uses a sequential change point detection method. The general change point detection method uses the known pre- and post-damage feature distributions to perform a sequential hypothesis test. In practice, however, the post-damage distribution is unlikely to be known a priori, unless we are looking for a known specific type of damage. Therefore, we introduce an additional algorithm that estimates and updates this distribution as data are collected using the maximum likelihood and the Bayesian methods. We also applied an approximate method to reduce the computation load and memory requirement associated with the estimation. The algorithm is validated using a set of experimental data collected from a four-story steel special moment-resisting frame and multiple sets of simulated data. Various features of different dimensions have been explored, and the algorithm was able to identify damage, particularly when it uses multidimensional damage sensitive features and lower false alarm rates, with a known post-damage feature distribution. For unknown feature distribution cases, the post-damage distribution was consistently estimated and the detection delays were only a few time steps longer than the delays from the general method that assumes we know the post-damage feature distribution. We confirmed that the Bayesian method is particularly efficient in declaring damage with minimal memory requirement, but the maximum likelihood method provides an insightful heuristic approach.

Machine Learning Classification to Identify the Stage of Brain-Computer Interface Therapy for Stroke Rehabilitation Using Functional Connectivity.

PubMed

Mohanty, Rosaleena; Sinha, Anita M; Remsik, Alexander B; Dodd, Keith C; Young, Brittany M; Jacobson, Tyler; McMillan, Matthew; Thoma, Jaclyn; Advani, Hemali; Nair, Veena A; Kang, Theresa J; Caldera, Kristin; Edwards, Dorothy F; Williams, Justin C; Prabhakaran, Vivek

2018-01-01

Interventional therapy using brain-computer interface (BCI) technology has shown promise in facilitating motor recovery in stroke survivors; however, the impact of this form of intervention on functional networks outside of the motor network specifically is not well-understood. Here, we investigated resting-state functional connectivity (rs-FC) in stroke participants undergoing BCI therapy across stages, namely pre- and post-intervention, to identify discriminative functional changes using a machine learning classifier with the goal of categorizing participants into one of the two therapy stages. Twenty chronic stroke participants with persistent upper-extremity motor impairment received neuromodulatory training using a closed-loop neurofeedback BCI device, and rs-functional MRI (rs-fMRI) scans were collected at four time points: pre-, mid-, post-, and 1 month post-therapy. To evaluate the peak effects of this intervention, rs-FC was analyzed from two specific stages, namely pre- and post-therapy. In total, 236 seeds spanning both motor and non-motor regions of the brain were computed at each stage. A univariate feature selection was applied to reduce the number of features followed by a principal component-based data transformation used by a linear binary support vector machine (SVM) classifier to classify each participant into a therapy stage. The SVM classifier achieved a cross-validation accuracy of 92.5% using a leave-one-out method. Outside of the motor network, seeds from the fronto-parietal task control, default mode, subcortical, and visual networks emerged as important contributors to the classification. Furthermore, a higher number of functional changes were observed to be strengthening from the pre- to post-therapy stage than the ones weakening, both of which involved motor and non-motor regions of the brain. These findings may provide new evidence to support the potential clinical utility of BCI therapy as a form of stroke rehabilitation that not only benefits motor recovery but also facilitates recovery in other brain networks. Moreover, delineation of stronger and weaker changes may inform more optimal designs of BCI interventional therapy so as to facilitate strengthened and suppress weakened changes in the recovery process.

Accuracy of land use change detection using support vector machine and maximum likelihood techniques for open-cast coal mining areas.

PubMed

Karan, Shivesh Kishore; Samadder, Sukha Ranjan

2016-08-01

One objective of the present study was to evaluate the performance of support vector machine (SVM)-based image classification technique with the maximum likelihood classification (MLC) technique for a rapidly changing landscape of an open-cast mine. The other objective was to assess the change in land use pattern due to coal mining from 2006 to 2016. Assessing the change in land use pattern accurately is important for the development and monitoring of coalfields in conjunction with sustainable development. For the present study, Landsat 5 Thematic Mapper (TM) data of 2006 and Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) data of 2016 of a part of Jharia Coalfield, Dhanbad, India, were used. The SVM classification technique provided greater overall classification accuracy when compared to the MLC technique in classifying heterogeneous landscape with limited training dataset. SVM exceeded MLC in handling a difficult challenge of classifying features having near similar reflectance on the mean signature plot, an improvement of over 11 % was observed in classification of built-up area, and an improvement of 24 % was observed in classification of surface water using SVM; similarly, the SVM technique improved the overall land use classification accuracy by almost 6 and 3 % for Landsat 5 and Landsat 8 images, respectively. Results indicated that land degradation increased significantly from 2006 to 2016 in the study area. This study will help in quantifying the changes and can also serve as a basis for further decision support system studies aiding a variety of purposes such as planning and management of mines and environmental impact assessment.

Spectral pattern classification in lidar data for rock identification in outcrops.

PubMed

Campos Inocencio, Leonardo; Veronez, Mauricio Roberto; Wohnrath Tognoli, Francisco Manoel; de Souza, Marcelo Kehl; da Silva, Reginaldo Macedônio; Gonzaga, Luiz; Blum Silveira, César Leonardo

2014-01-01

The present study aimed to develop and implement a method for detection and classification of spectral signatures in point clouds obtained from terrestrial laser scanner in order to identify the presence of different rocks in outcrops and to generate a digital outcrop model. To achieve this objective, a software based on cluster analysis was created, named K-Clouds. This software was developed through a partnership between UNISINOS and the company V3D. This tool was designed to begin with an analysis and interpretation of a histogram from a point cloud of the outcrop and subsequently indication of a number of classes provided by the user, to process the intensity return values. This classified information can then be interpreted by geologists, to provide a better understanding and identification from the existing rocks in the outcrop. Beyond the detection of different rocks, this work was able to detect small changes in the physical-chemical characteristics of the rocks, as they were caused by weathering or compositional changes.

Resolving dust emission responses to land cover change using an ecological land classification

USDA-ARS?s Scientific Manuscript database

Despite efforts to quantify the impacts of land cover change on wind erosion, assessment uncertainty remains large. We address this uncertainty by evaluating the application of ecological site concepts and state-and-transition models (STMs) for detecting and quantitatively describing the impacts of ...

Object detection in cinematographic video sequences for automatic indexing

NASA Astrophysics Data System (ADS)

Stauder, Jurgen; Chupeau, Bertrand; Oisel, Lionel

2003-06-01

This paper presents an object detection framework applied to cinematographic post-processing of video sequences. Post-processing is done after production and before editing. At the beginning of each shot of a video, a slate (also called clapperboard) is shown. The slate contains notably an electronic audio timecode that is necessary for audio-visual synchronization. This paper presents an object detection framework to detect slates in video sequences for automatic indexing and post-processing. It is based on five steps. The first two steps aim to reduce drastically the video data to be analyzed. They ensure high recall rate but have low precision. The first step detects images at the beginning of a shot possibly showing up a slate while the second step searches in these images for candidates regions with color distribution similar to slates. The objective is to not miss any slate while eliminating long parts of video without slate appearance. The third and fourth steps are statistical classification and pattern matching to detected and precisely locate slates in candidate regions. These steps ensure high recall rate and high precision. The objective is to detect slates with very little false alarms to minimize interactive corrections. In a last step, electronic timecodes are read from slates to automize audio-visual synchronization. The presented slate detector has a recall rate of 89% and a precision of 97,5%. By temporal integration, much more than 89% of shots in dailies are detected. By timecode coherence analysis, the precision can be raised too. Issues for future work are to accelerate the system to be faster than real-time and to extend the framework for several slate types.

Esophageal motor disorders are frequent during pre and post lung transplantation. Can they influence lung rejection?

PubMed

Ciriza de Los Ríos, Constanza; Canga Rodríguez-Valcárcel, Fernando; de Pablo Gafas, Alicia; Castel de Lucas, Isabel; Lora Pablos, David; Castellano Tortajada, Gregorio

2018-06-01

lung transplantation (LTx) is a viable option for most patients with end-stage lung diseases. Esophageal motor disorders (EMD) are frequent in candidates for LTx, but there is very little data about changes in esophageal motility post-LTx. the aim of our study was to assess esophageal motor disorders by high resolution manometry (HRM) both pre-LTx and six months post-LTx in patients with and without organ rejection. HRM (Manoscan®) was performed in 57 patients both pre-LTx and six months post-LTx. HRM plots were analyzed according to the Chicago classification 3.0. EMD were found in 33.3% and in 49.1% of patients pre-LTx and post-LTx, respectively, and abnormal peristalsis was more frequently found post-LTx (p = 0.018). Hypercontractile esophagus was frequently found post-LTx (1.8% and 19.3% pre-LTx and post-LTx, respectively). Esophagogastric junction (EGJ) morphology changed significantly pre-LTx and post-LTx; type I (normal) was more frequent post-LTx (63-2% and 82.5% respectively, p = 0.007). EMD were more frequent post-LTx in both the non-rejection and rejection group, although particularly in the rejection group (43.2% and 69.2% respectively, p = 0.09). EMD such as distal spasm, hypercontractile esophagus and EGJ outflow obstruction were also observed more frequently post-LTx in the rejection group. significant changes in esophageal motility were observed pre-LTx and particularly post-LTx; hypercontractile esophagus was a frequent EMD found post-LTx. EMD were more frequent in the group of patients that experienced organ rejection compared to the non-rejection group. EMD leading to an impaired esophageal clearance should be considered as an additional factor that contributes to LTx failure.

A comparison of change detection methods using multispectral scanner data

USGS Publications Warehouse

Seevers, Paul M.; Jones, Brenda K.; Qiu, Zhicheng; Liu, Yutong

1994-01-01

Change detection methods were investigated as a cooperative activity between the U.S. Geological Survey and the National Bureau of Surveying and Mapping, People's Republic of China. Subtraction of band 2, band 3, normalized difference vegetation index, and tasseled cap bands 1 and 2 data from two multispectral scanner images were tested using two sites in the United States and one in the People's Republic of China. A new statistical method also was tested. Band 2 subtraction gives the best results for detecting change from vegetative cover to urban development. The statistical method identifies areas that have changed and uses a fast classification algorithm to classify the original data of the changed areas by land cover type present for each image date.

Satellite inventory of Minnesota forest resources

NASA Technical Reports Server (NTRS)

Bauer, Marvin E.; Burk, Thomas E.; Ek, Alan R.; Coppin, Pol R.; Lime, Stephen D.; Walsh, Terese A.; Walters, David K.; Befort, William; Heinzen, David F.

1993-01-01

The methods and results of using Landsat Thematic Mapper (TM) data to classify and estimate the acreage of forest covertypes in northeastern Minnesota are described. Portions of six TM scenes covering five counties with a total area of 14,679 square miles were classified into six forest and five nonforest classes. The approach involved the integration of cluster sampling, image processing, and estimation. Using cluster sampling, 343 plots, each 88 acres in size, were photo interpreted and field mapped as a source of reference data for classifier training and calibration of the TM data classifications. Classification accuracies of up to 75 percent were achieved; most misclassification was between similar or related classes. An inverse method of calibration, based on the error rates obtained from the classifications of the cluster plots, was used to adjust the classification class proportions for classification errors. The resulting area estimates for total forest land in the five-county area were within 3 percent of the estimate made independently by the USDA Forest Service. Area estimates for conifer and hardwood forest types were within 0.8 and 6.0 percent respectively, of the Forest Service estimates. A trial of a second method of estimating the same classes as the Forest Service resulted in standard errors of 0.002 to 0.015. A study of the use of multidate TM data for change detection showed that forest canopy depletion, canopy increment, and no change could be identified with greater than 90 percent accuracy. The project results have been the basis for the Minnesota Department of Natural Resources and the Forest Service to define and begin to implement an annual system of forest inventory which utilizes Landsat TM data to detect changes in forest cover.

Spatial Analysis to Determine Paddy Field Changes in Indonesia: A Case Study in Suburban Areas of Jakarta

NASA Astrophysics Data System (ADS)

Putri Utami, Nadia; Ahamed, Tofael

2018-05-01

Karawang, a suburban area of Greater Jakarta, is known as the second largest rice-producing region in West Java, Indonesia. However, expansion of urban sprawl and industrial area from Greater Jakarta have created rapid agricultural land use/cover changes, especially paddy field, in Karawang. This study analyzed the land use/cover changes of paddy field from 2000 to 2016. Landsat 4-5 TM and Landsat 8 OLI/TIRS images were acquired from USGS Earth Explorer, UTM zone 48 south. Satellite image pre-processing, ground truth data collection, supervised maximum likelihood classifications, and Post-Classification Comparison (PCC) were performed in ArcGIS 10.3®. It was observed between 2000 and 2016, urban area increased 4.46% (8530 ha) from initial area of 10,004 ha. Meanwhile paddy field decreased 3.18% (6091 ha) from initial area of 115,720 ha. The spatial analysis showed that paddy field in the fringe of urban area are more susceptible for changes.

An assessment of the cultivated cropland class of NLCD 2006 using a multi-source and multi-criteria approach

USGS Publications Warehouse

Danielson, Patrick; Yang, Limin; Jin, Suming; Homer, Collin G.; Napton, Darrell

2016-01-01

We developed a method that analyzes the quality of the cultivated cropland class mapped in the USA National Land Cover Database (NLCD) 2006. The method integrates multiple geospatial datasets and a Multi Index Integrated Change Analysis (MIICA) change detection method that captures spectral changes to identify the spatial distribution and magnitude of potential commission and omission errors for the cultivated cropland class in NLCD 2006. The majority of the commission and omission errors in NLCD 2006 are in areas where cultivated cropland is not the most dominant land cover type. The errors are primarily attributed to the less accurate training dataset derived from the National Agricultural Statistics Service Cropland Data Layer dataset. In contrast, error rates are low in areas where cultivated cropland is the dominant land cover. Agreement between model-identified commission errors and independently interpreted reference data was high (79%). Agreement was low (40%) for omission error comparison. The majority of the commission errors in the NLCD 2006 cultivated crops were confused with low-intensity developed classes, while the majority of omission errors were from herbaceous and shrub classes. Some errors were caused by inaccurate land cover change from misclassification in NLCD 2001 and the subsequent land cover post-classification process.

Identification, Characterization, and Utilization of Adult Meniscal Progenitor Cells

DTIC Science & Technology

2015-09-01

the development of knee osteoarthritis (OA). New treatments centered on the stem/progenitor cell population resident within the adult meniscus will be...cells, stem cells, progenitor cells, meniscus healing, meniscus repair, osteoarthritis 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT...changes that occur after injury. As a result, meniscal injuries are a common underlying cause of post-traumatic osteoarthritis . This is particularly

Advancing Bag-of-Visual-Words Representations for Lesion Classification in Retinal Images

PubMed Central

Pires, Ramon; Jelinek, Herbert F.; Wainer, Jacques; Valle, Eduardo; Rocha, Anderson

2014-01-01

Diabetic Retinopathy (DR) is a complication of diabetes that can lead to blindness if not readily discovered. Automated screening algorithms have the potential to improve identification of patients who need further medical attention. However, the identification of lesions must be accurate to be useful for clinical application. The bag-of-visual-words (BoVW) algorithm employs a maximum-margin classifier in a flexible framework that is able to detect the most common DR-related lesions such as microaneurysms, cotton-wool spots and hard exudates. BoVW allows to bypass the need for pre- and post-processing of the retinographic images, as well as the need of specific ad hoc techniques for identification of each type of lesion. An extensive evaluation of the BoVW model, using three large retinograph datasets (DR1, DR2 and Messidor) with different resolution and collected by different healthcare personnel, was performed. The results demonstrate that the BoVW classification approach can identify different lesions within an image without having to utilize different algorithms for each lesion reducing processing time and providing a more flexible diagnostic system. Our BoVW scheme is based on sparse low-level feature detection with a Speeded-Up Robust Features (SURF) local descriptor, and mid-level features based on semi-soft coding with max pooling. The best BoVW representation for retinal image classification was an area under the receiver operating characteristic curve (AUC-ROC) of 97.8% (exudates) and 93.5% (red lesions), applying a cross-dataset validation protocol. To assess the accuracy for detecting cases that require referral within one year, the sparse extraction technique associated with semi-soft coding and max pooling obtained an AUC of 94.22.0%, outperforming current methods. Those results indicate that, for retinal image classification tasks in clinical practice, BoVW is equal and, in some instances, surpasses results obtained using dense detection (widely believed to be the best choice in many vision problems) for the low-level descriptors. PMID:24886780

Assessment of land cover changes in Lampedusa Island (Italy) using Landsat TM and OLI data

NASA Astrophysics Data System (ADS)

Mei, Alessandro; Manzo, Ciro; Fontinovo, Giuliano; Bassani, Cristiana; Allegrini, Alessia; Petracchini, Francesco

2016-10-01

The Lampedusa Island displays important socio-economic criticalities related to an intensive touristic activity, which implies an increase in electricity consumption and waste production. An adequate island conversion to a more environmental, sustainable community needs to be faced by the local Management Plans establishment. For this purpose, several thematic datasets have to be produced and evaluated. Socio-economic and bio-ecological components as well as land cover/use assessment are some of the main topics to be managed within the Decision Support Systems. Considering the lack of Land Cover (LC) and vegetation change detection maps in Lampedusa Island (Italy), this paper focuses on the retrieval of these topics by remote sensing techniques. The analysis was carried out by Landsat 5 TM and Landsat 8 OLI multispectral images from 1984 to 2014 in order to obtain spatial and temporal information of changes occurred in the island. Firstly, imagery was co-registered and atmospherically corrected; secondly, it was then classified for land cover and vegetation distribution analysis with the use of QGIS and Saga GIS open source softwares. The Maximum Likelihood Classifier (MLC) was used for LC maps production, while the Normalized Difference Vegetation Index (NDVI) was used for vegetation examination and distribution. Topographic maps, historical aerial photos, ortophotos and field data are merged in the GIS for accuracy assessment. Finally, change detection of MLC and NDVI are provided respectively by Post-Classification Comparison (PCC) and Image Differencing (ID). The provided information, combined with local socio-economic parameters, is essential for the improvement of environmental sustainability of anthropogenic activities in Lampedusa.

Delineation of marsh types and marsh-type change in coastal Louisiana for 2007 and 2013

USGS Publications Warehouse

Hartley, Stephen B.; Couvillion, Brady R.; Enwright, Nicholas M.

2017-05-30

The Bureau of Ocean Energy Management researchers often require detailed information regarding emergent marsh vegetation types (such as fresh, intermediate, brackish, and saline) for modeling habitat capacities and mitigation. In response, the U.S. Geological Survey in cooperation with the Bureau of Ocean Energy Management produced a detailed change classification of emergent marsh vegetation types in coastal Louisiana from 2007 and 2013. This study incorporates two existing vegetation surveys and independent variables such as Landsat Thematic Mapper multispectral satellite imagery, high-resolution airborne imagery from 2007 and 2013, bare-earth digital elevation models based on airborne light detection and ranging, alternative contemporary land-cover classifications, and other spatially explicit variables. An image classification based on image objects was created from 2007 and 2013 National Agriculture Imagery Program color-infrared aerial photography. The final products consisted of two 10-meter raster datasets. Each image object from the 2007 and 2013 spatial datasets was assigned a vegetation classification by using a simple majority filter. In addition to those spatial datasets, we also conducted a change analysis between the datasets to produce a 10-meter change raster product. This analysis identified how much change has taken place and where change has occurred. The spatial data products show dynamic areas where marsh loss is occurring or where marsh type is changing. This information can be used to assist and advance conservation efforts for priority natural resources.

76 FR 66184 - Post Office Organization and Administration: Establishment, Classification, and Discontinuance

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-26

... Service published a proposed rule in the Federal Register (76 FR 17794) to improve the process for... POSTAL SERVICE 39 CFR Part 241 Post Office Organization and Administration: Establishment, Classification, and Discontinuance AGENCY: Postal Service. ACTION: Final rule. SUMMARY: The Postal Service is...

A Randomized Clinical Trial of Cognitive-Behavioral Treatment for PTSD in Women

DTIC Science & Technology

2003-10-01

Post Traumatic Stress Disorder ( PTSD ) in 384 female veterans and active duty personnel at 11 sites. This is a VA Cooperative Study. Walter...14. SUBJECT TERMS 15. NUMBER OF PAGES Post - Traumatic Stress Disorder 6 16. PRICE CODE 17. SECURITY CLASSIFICATION 18. SECURITY CLASSIFICATION 19...Clinical Trial of Cognitive-Behavioral Treatment for Post Traumatic Stress Disorder in Women for this study, from the protocol Additionally, a new

A novel evaluation of two related and two independent algorithms for eye movement classification during reading.

PubMed

Friedman, Lee; Rigas, Ioannis; Abdulin, Evgeny; Komogortsev, Oleg V

2018-05-15

Nystrӧm and Holmqvist have published a method for the classification of eye movements during reading (ONH) (Nyström & Holmqvist, 2010). When we applied this algorithm to our data, the results were not satisfactory, so we modified the algorithm (now the MNH) to better classify our data. The changes included: (1) reducing the amount of signal filtering, (2) excluding a new type of noise, (3) removing several adaptive thresholds and replacing them with fixed thresholds, (4) changing the way that the start and end of each saccade was determined, (5) employing a new algorithm for detecting PSOs, and (6) allowing a fixation period to either begin or end with noise. A new method for the evaluation of classification algorithms is presented. It was designed to provide comprehensive feedback to an algorithm developer, in a time-efficient manner, about the types and numbers of classification errors that an algorithm produces. This evaluation was conducted by three expert raters independently, across 20 randomly chosen recordings, each classified by both algorithms. The MNH made many fewer errors in determining when saccades start and end, and it also detected some fixations and saccades that the ONH did not. The MNH fails to detect very small saccades. We also evaluated two additional algorithms: the EyeLink Parser and a more current, machine-learning-based algorithm. The EyeLink Parser tended to find more saccades that ended too early than did the other methods, and we found numerous problems with the output of the machine-learning-based algorithm.

The Analysis of Object-Based Change Detection in Mining Area: a Case Study with Pingshuo Coal Mine

NASA Astrophysics Data System (ADS)

Zhang, M.; Zhou, W.; Li, Y.

2017-09-01

Accurate information on mining land use and land cover change are crucial for monitoring and environmental change studies. In this paper, RapidEye Remote Sensing Image (Map 2012) and SPOT7 Remote Sensing Image (Map 2015) in Pingshuo Mining Area are selected to monitor changes combined with object-based classification and change vector analysis method, we also used R in highresolution remote sensing image for mining land classification, and found the feasibility and the flexibility of open source software. The results show that (1) the classification of reclaimed mining land has higher precision, the overall accuracy and kappa coefficient of the classification of the change region map were 86.67 % and 89.44 %. It's obvious that object-based classification and change vector analysis which has a great significance to improve the monitoring accuracy can be used to monitor mining land, especially reclaiming mining land; (2) the vegetation area changed from 46 % to 40 % accounted for the proportion of the total area from 2012 to 2015, and most of them were transformed into the arable land. The sum of arable land and vegetation area increased from 51 % to 70 %; meanwhile, build-up land has a certain degree of increase, part of the water area was transformed into arable land, but the extent of the two changes is not obvious. The result illustrated the transformation of reclaimed mining area, at the same time, there is still some land convert to mining land, and it shows the mine is still operating, mining land use and land cover are the dynamic procedure.

Land Cover Changes between 1974 and 2008 in Ulaanbaatar, Mongolia

NASA Astrophysics Data System (ADS)

Bagan, H.; Kinoshita, T.; Yamagata, Y.

2009-12-01

In the past 35 years, a combination of human actions and natural causes has led to a significant decline in land quality in Ulaanbaatar, the capital city of Mongolia. Human causes include changes in conventional livestock husbandry, overgrazing, and exploitation for traditional uses. Natural causes include a harsh, dry climate, short growing seasons, and thin soils. Since 1995, many herders left the countryside to come to the city in search of new opportunities, the Ger areas (wooden houses and Ger) have expended, resulting in urban sprawl. Since urbanization usually advance in an uncontrolled or unorganized way in Mongolia, they have destructive effects on the environment, particularly on basic ecosystems, wildlife habitat, and pollution of natural resources (e.g. air and water). Land use and land cover changes occurred in the region are investigated using satellite images acquired in 1974 (Landsat MSS), 1990 (Landsat TM), 2000 (ASTER), 2006 (IKONOS), and 2008 (ALOS). Pre-processing of all data included orthorectification and registration to precisely geolocated imagery. In the detection of changes, classification approaches were employed using a self-organizing map (SOM) neural network classifier (Fig. 1a) and new developed subspace classification method (Fig. 1b). From the time-series classified remote sensing images, we extract the land cover and land cover temporal changes from 1974 to 2008. The results show some important findings regarding the size and nature of the change occurred in the study area. A significant amount of steppe and forest lands have been destroyed or replaced by residential areas; as a result, the total area of urban region doubled in the 35-year period with a higher urbanization rate between 2000 and 2008. Key words: Environment; Land Cover; Urban; Change detection; Classification. References Chinbat,B., Bayantur,M., & Amarsaikhan.D. (2006). Investigation of the internal structure changes of ulaanbaatar city using RS and GIS. ISPRS Commission VII Mid-term Symposium “Remote Sensing: From Pixels to Processes”, Enschede, the Netherlands, 8-11 May 2006. 511-516. Bagan, H., Wang, Q., Watanabe, M., Karneyarna, S., & Bao, Y. (2008). Land-cover classification using ASTER multi-band combinations based on wavelet fusion and SOM neural network. Photogrammetric Engineering and Remote Sensing, 74, 333-342. Bagan, H., Yasuoka, Y., Endo, T., Wang, X., & Feng, Z. (2008). Classification of airborne hyperspectral data based on the average learning subspace method. IEEE Geoscience and Remote Sensing Letters, 5, 368-372. Figure 1. The self-organizing map (SOM) neural network classifier (a) and the subspace classification method (b).

From Google Maps to a fine-grained catalog of street trees

NASA Astrophysics Data System (ADS)

Branson, Steve; Wegner, Jan Dirk; Hall, David; Lang, Nico; Schindler, Konrad; Perona, Pietro

2018-01-01

Up-to-date catalogs of the urban tree population are of importance for municipalities to monitor and improve quality of life in cities. Despite much research on automation of tree mapping, mainly relying on dedicated airborne LiDAR or hyperspectral campaigns, tree detection and species recognition is still mostly done manually in practice. We present a fully automated tree detection and species recognition pipeline that can process thousands of trees within a few hours using publicly available aerial and street view images of Google MapsTM. These data provide rich information from different viewpoints and at different scales from global tree shapes to bark textures. Our work-flow is built around a supervised classification that automatically learns the most discriminative features from thousands of trees and corresponding, publicly available tree inventory data. In addition, we introduce a change tracker that recognizes changes of individual trees at city-scale, which is essential to keep an urban tree inventory up-to-date. The system takes street-level images of the same tree location at two different times and classifies the type of change (e.g., tree has been removed). Drawing on recent advances in computer vision and machine learning, we apply convolutional neural networks (CNN) for all classification tasks. We propose the following pipeline: download all available panoramas and overhead images of an area of interest, detect trees per image and combine multi-view detections in a probabilistic framework, adding prior knowledge; recognize fine-grained species of detected trees. In a later, separate module, track trees over time, detect significant changes and classify the type of change. We believe this is the first work to exploit publicly available image data for city-scale street tree detection, species recognition and change tracking, exhaustively over several square kilometers, respectively many thousands of trees. Experiments in the city of Pasadena, California, USA show that we can detect >70% of the street trees, assign correct species to >80% for 40 different species, and correctly detect and classify changes in >90% of the cases.

Aneurysmal subarachnoid hemorrhage prognostic decision-making algorithm using classification and regression tree analysis.

PubMed

Lo, Benjamin W Y; Fukuda, Hitoshi; Angle, Mark; Teitelbaum, Jeanne; Macdonald, R Loch; Farrokhyar, Forough; Thabane, Lehana; Levine, Mitchell A H

2016-01-01

Classification and regression tree analysis involves the creation of a decision tree by recursive partitioning of a dataset into more homogeneous subgroups. Thus far, there is scarce literature on using this technique to create clinical prediction tools for aneurysmal subarachnoid hemorrhage (SAH). The classification and regression tree analysis technique was applied to the multicenter Tirilazad database (3551 patients) in order to create the decision-making algorithm. In order to elucidate prognostic subgroups in aneurysmal SAH, neurologic, systemic, and demographic factors were taken into account. The dependent variable used for analysis was the dichotomized Glasgow Outcome Score at 3 months. Classification and regression tree analysis revealed seven prognostic subgroups. Neurological grade, occurrence of post-admission stroke, occurrence of post-admission fever, and age represented the explanatory nodes of this decision tree. Split sample validation revealed classification accuracy of 79% for the training dataset and 77% for the testing dataset. In addition, the occurrence of fever at 1-week post-aneurysmal SAH is associated with increased odds of post-admission stroke (odds ratio: 1.83, 95% confidence interval: 1.56-2.45, P < 0.01). A clinically useful classification tree was generated, which serves as a prediction tool to guide bedside prognostication and clinical treatment decision making. This prognostic decision-making algorithm also shed light on the complex interactions between a number of risk factors in determining outcome after aneurysmal SAH.

Long-term passive acoustic recordings track the changing distribution of North Atlantic right whales (Eubalaena glacialis) from 2004 to 2014.

PubMed

Davis, Genevieve E; Baumgartner, Mark F; Bonnell, Julianne M; Bell, Joel; Berchok, Catherine; Bort Thornton, Jacqueline; Brault, Solange; Buchanan, Gary; Charif, Russell A; Cholewiak, Danielle; Clark, Christopher W; Corkeron, Peter; Delarue, Julien; Dudzinski, Kathleen; Hatch, Leila; Hildebrand, John; Hodge, Lynne; Klinck, Holger; Kraus, Scott; Martin, Bruce; Mellinger, David K; Moors-Murphy, Hilary; Nieukirk, Sharon; Nowacek, Douglas P; Parks, Susan; Read, Andrew J; Rice, Aaron N; Risch, Denise; Širović, Ana; Soldevilla, Melissa; Stafford, Kate; Stanistreet, Joy E; Summers, Erin; Todd, Sean; Warde, Ann; Van Parijs, Sofie M

2017-10-18

Given new distribution patterns of the endangered North Atlantic right whale (NARW; Eubalaena glacialis) population in recent years, an improved understanding of spatio-temporal movements are imperative for the conservation of this species. While so far visual data have provided most information on NARW movements, passive acoustic monitoring (PAM) was used in this study in order to better capture year-round NARW presence. This project used PAM data from 2004 to 2014 collected by 19 organizations throughout the western North Atlantic Ocean. Overall, data from 324 recorders (35,600 days) were processed and analyzed using a classification and detection system. Results highlight almost year-round habitat use of the western North Atlantic Ocean, with a decrease in detections in waters off Cape Hatteras, North Carolina in summer and fall. Data collected post 2010 showed an increased NARW presence in the mid-Atlantic region and a simultaneous decrease in the northern Gulf of Maine. In addition, NARWs were widely distributed across most regions throughout winter months. This study demonstrates that a large-scale analysis of PAM data provides significant value to understanding and tracking shifts in large whale movements over long time scales.

Obtaining Accurate Probabilities Using Classifier Calibration

ERIC Educational Resources Information Center

Pakdaman Naeini, Mahdi

2016-01-01

Learning probabilistic classification and prediction models that generate accurate probabilities is essential in many prediction and decision-making tasks in machine learning and data mining. One way to achieve this goal is to post-process the output of classification models to obtain more accurate probabilities. These post-processing methods are…

Visual Impairment/lntracranial Pressure Risk Clinical Care Data Tools

NASA Technical Reports Server (NTRS)

Van Baalen, Mary; Mason, Sara S.; Taiym, Wafa; Wear, Mary L.; Moynihan, Shannan; Alexander, David; Hart, Steve; Tarver, William

2014-01-01

Prior to 2010, several ISS crewmembers returned from spaceflight with changes to their vision, ranging from a mild hyperopic shift to frank disc edema. As a result, NASA expanded clinical vision testing to include more comprehensive medical imaging, including Optical Coherence Tomography and 3 Tesla Brain and Orbit MRIs. The Space and Clinical Operations (SCO) Division developed a clinical practice guideline that classified individuals based on their symptoms and diagnoses to facilitate clinical care. For the purposes of clinical surveillance, this classification was applied retrospectively to all crewmembers who had sufficient testing for classification. This classification is also a tool that has been leveraged for researchers to identify potential risk factors. In March 2014, driven in part by a more comprehensive understanding of the imaging data and increased imaging capability on orbit, the SCO Division revised their clinical care guidance to outline in-flight care and increase post-flight follow up. The new clinical guidance does not include a classification scheme

An Online 3D Database System for Endangered Architectural and Archaeological Heritage in the South-Eastern Mediterranean

NASA Astrophysics Data System (ADS)

Abate, D.; Avgousti, A.; Faka, M.; Hermon, S.; Bakirtzis, N.; Christofi, P.

2017-10-01

This study compares performance of aerial image based point clouds (IPCs) and light detection and ranging (LiDAR) based point clouds in detection of thinnings and clear cuts in forests. IPCs are an appealing method to update forest resource data, because of their accuracy in forest height estimation and cost-efficiency of aerial image acquisition. We predicted forest changes over a period of three years by creating difference layers that displayed the difference in height or volume between the initial and subsequent time points. Both IPCs and LiDAR data were used in this process. The IPCs were constructed with the Semi-Global Matching (SGM) algorithm. Difference layers were constructed by calculating differences in fitted height or volume models or in canopy height models (CHMs) from both time points. The LiDAR-derived digital terrain model (DTM) was used to scale heights to above ground level. The study area was classified in logistic regression into the categories ClearCut, Thinning or NoChange with the values from the difference layers. We compared the predicted changes with the true changes verified in the field, and obtained at best a classification accuracy for clear cuts 93.1 % with IPCs and 91.7 % with LiDAR data. However, a classification accuracy for thinnings was only 8.0 % with IPCs. With LiDAR data 41.4 % of thinnings were detected. In conclusion, the LiDAR data proved to be more accurate method to predict the minor changes in forests than IPCs, but both methods are useful in detection of major changes.

Reconstruction of chaotic saddles by classification of unstable periodic orbits: Kuramoto-Sivashinsky equation

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

Saiki, Yoshitaka, E-mail: yoshi.saiki@r.hit-u.ac.jp; Yamada, Michio; Chian, Abraham C.-L.

The unstable periodic orbits (UPOs) embedded in a chaotic attractor after an attractor merging crisis (MC) are classified into three subsets, and employed to reconstruct chaotic saddles in the Kuramoto-Sivashinsky equation. It is shown that in the post-MC regime, the two chaotic saddles evolved from the two coexisting chaotic attractors before crisis can be reconstructed from the UPOs embedded in the pre-MC chaotic attractors. The reconstruction also involves the detection of the mediating UPO responsible for the crisis, and the UPOs created after crisis that fill the gap regions of the chaotic saddles. We show that the gap UPOs originatemore » from saddle-node, period-doubling, and pitchfork bifurcations inside the periodic windows in the post-MC chaotic region of the bifurcation diagram. The chaotic attractor in the post-MC regime is found to be the closure of gap UPOs.« less

Reconstruction of chaotic saddles by classification of unstable periodic orbits: Kuramoto-Sivashinsky equation.

PubMed

Saiki, Yoshitaka; Yamada, Michio; Chian, Abraham C-L; Miranda, Rodrigo A; Rempel, Erico L

2015-10-01

The unstable periodic orbits (UPOs) embedded in a chaotic attractor after an attractor merging crisis (MC) are classified into three subsets, and employed to reconstruct chaotic saddles in the Kuramoto-Sivashinsky equation. It is shown that in the post-MC regime, the two chaotic saddles evolved from the two coexisting chaotic attractors before crisis can be reconstructed from the UPOs embedded in the pre-MC chaotic attractors. The reconstruction also involves the detection of the mediating UPO responsible for the crisis, and the UPOs created after crisis that fill the gap regions of the chaotic saddles. We show that the gap UPOs originate from saddle-node, period-doubling, and pitchfork bifurcations inside the periodic windows in the post-MC chaotic region of the bifurcation diagram. The chaotic attractor in the post-MC regime is found to be the closure of gap UPOs.

ECG Based Heart Arrhythmia Detection Using Wavelet Coherence and Bat Algorithm

NASA Astrophysics Data System (ADS)

Kora, Padmavathi; Sri Rama Krishna, K.

2016-12-01

Atrial fibrillation (AF) is a type of heart abnormality, during the AF electrical discharges in the atrium are rapid, results in abnormal heart beat. The morphology of ECG changes due to the abnormalities in the heart. This paper consists of three major steps for the detection of heart diseases: signal pre-processing, feature extraction and classification. Feature extraction is the key process in detecting the heart abnormality. Most of the ECG detection systems depend on the time domain features for cardiac signal classification. In this paper we proposed a wavelet coherence (WTC) technique for ECG signal analysis. The WTC calculates the similarity between two waveforms in frequency domain. Parameters extracted from WTC function is used as the features of the ECG signal. These features are optimized using Bat algorithm. The Levenberg Marquardt neural network classifier is used to classify the optimized features. The performance of the classifier can be improved with the optimized features.

Change detection and change monitoring of natural and man-made features in multispectral and hyperspectral satellite imagery

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

Moody, Daniela Irina

An approach for land cover classification, seasonal and yearly change detection and monitoring, and identification of changes in man-made features may use a clustering of sparse approximations (CoSA) on sparse representations in learned dictionaries. A Hebbian learning rule may be used to build multispectral or hyperspectral, multiresolution dictionaries that are adapted to regional satellite image data. Sparse image representations of pixel patches over the learned dictionaries may be used to perform unsupervised k-means clustering into land cover categories. The clustering process behaves as a classifier in detecting real variability. This approach may combine spectral and spatial textural characteristics to detectmore » geologic, vegetative, hydrologic, and man-made features, as well as changes in these features over time.« less

Correlating Flight Behavior and Radar Measurements for Species Based Classification of Bird Radar Echoes for Wind Energy Site Assessment

NASA Astrophysics Data System (ADS)

Werth, S. P.; Frasier, S. J.

2015-12-01

Wind energy is one of the fastest-growing segments of the world energy market, offering a clean and abundant source of electricity. However, wind energy facilities can have detrimental effects on wildlife, especially birds and bats. Monitoring systems based on marine navigation radar are often used to quantify migration near potential wind sites, but the ability to reliably distinguish between bats and different varieties of birds has not been practically achieved. This classification capability would enable wind site selection that protects more vulnerable species, such as bats and raptors. Flight behavior, such as wing beat frequency, changes in speed, or changes in orientation, are known to vary by species [1]. The ability to extract these properties from radar data could ultimately enable a species based classification scheme. In this work, we analyze the relationship between radar measurements and bird flight behavior in echoes from avifauna. During the 2014 fall migration season, the UMass dual polarized weather radar was used to collect low elevation observations of migrating birds as they traversed through a fixed antenna beam. The radar was run during the night time, in clear-air conditions. Data was coherently integrated, and detections of biological targets exceeding an SNR threshold were extracted. Detections without some dominant frequency content (i.e. clear periodicity, potentially the wing beat frequency) were removed from the sample in order to isolate observations suspected to contain a single species or bird. For the remaining detections, measurements including the polarimetric products and the Doppler spectrum were extracted at each time step over the duration of the observation. The periodic and time changing nature of some of these different measurements was found to have a strong correlation with flight behavior (i.e. flapping vs. gliding behavior). Assumptions about flight behavior and orientation were corroborated through scattering simulations of birds in flight. The presence of a strong correlation between certain radar measurements and flight behavior would suggest the potential for a broad, species based avian classification algorithm. Such a classification scheme could ultimately help select and monitor wind sites in order to minimize harm to at-risk bird and bat species.

Molecular classification of fatty liver by high-throughput profiling of protein post-translational modifications.

PubMed

Urasaki, Yasuyo; Fiscus, Ronald R; Le, Thuc T

2016-04-01

We describe an alternative approach to classifying fatty liver by profiling protein post-translational modifications (PTMs) with high-throughput capillary isoelectric focusing (cIEF) immunoassays. Four strains of mice were studied, with fatty livers induced by different causes, such as ageing, genetic mutation, acute drug usage, and high-fat diet. Nutrient-sensitive PTMs of a panel of 12 liver metabolic and signalling proteins were simultaneously evaluated with cIEF immunoassays, using nanograms of total cellular protein per assay. Changes to liver protein acetylation, phosphorylation, and O-N-acetylglucosamine glycosylation were quantified and compared between normal and diseased states. Fatty liver tissues could be distinguished from one another by distinctive protein PTM profiles. Fatty liver is currently classified by morphological assessment of lipid droplets, without identifying the underlying molecular causes. In contrast, high-throughput profiling of protein PTMs has the potential to provide molecular classification of fatty liver. Copyright © 2016 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Impact of 1985 hurricanes on Isles Dernieres, Louisiana: Temporal and spatial analysis of coastal geomorphic changes

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

Debusshere, K.; Westphal, K.; Penland, S.

1989-09-01

Catastrophic geomorphic changes occurred in the Isles Dernieres barrier island arc as a result of the direct impact of three hurricanes in 1985. The severity of the impact of hurricanes Danny, Elena, and Juan had not been equaled since the landfall of hurricanes Betsy and Camille in the late 1960s. The Isles Dernieres had not been subjected to a direct hurricane landfall since hurricane Bob in 1979. The recent hurricane impacts provided the USGS/LGS Louisiana Cooperative Barrier Island and Land Loss Study the opportunity to examine the process-response characteristics of this low-profile transgressive barrier island arc to multiple hurricane impactsmore » in a single hurricane season. The geomorphic changes along the Isles Dernieres were determined using four sequential airborne videotape surveys acquired in July 1984, July 1985 (pre-storm), August 1985 (post-Danny) and November 1985 (post-Juan) and mapped on 1:24,000 base maps produced from concurrent vertical aerial photography. A coastal geomorphic classification was developed to describe, quantify, and map the alongshore geomorphic, sedimentologic , and vegetative character of this barrier shoreline. The classification consists of three levels of descriptors: (1) primary morphology to define the predominant longshore morphology, (2) modifiers to depict the small-scale longshore features, and (3) variants to locate and quantify important coastal features, not mappable at the scale used.« less

Characterisation of the metabolome of ocular tissues and post-mortem changes in the rat retina.

PubMed

Tan, Shi Z; Mullard, Graham; Hollywood, Katherine A; Dunn, Warwick B; Bishop, Paul N

2016-08-01

Time-dependent post-mortem biochemical changes have been demonstrated in donor cornea and vitreous, but there have been no published studies to date that objectively measure post-mortem changes in the retinal metabolome over time. The aim of the study was firstly, to investigate post-mortem, time-dependent changes in the rat retinal metabolome and secondly, to compare the metabolite composition of healthy rat ocular tissues. To study post-mortem changes in the rat retinal metabolome, globes were enucleated and stored at 4 °C and sampled at 0, 2, 4, 8, 24 and 48 h post-mortem. To study the metabolite composition of rat ocular tissues, eyes were dissected immediately after culling to isolate the cornea, lens, vitreous and retina, prior to storing at -80 °C. Tissue extracts were subjected to Gas Chromatograph Mass Spectrometry (GC-MS) and Ultra High Performance Liquid Chromatography Mass Spectrometry (UHPLC-MS). Generally, the metabolic composition of the retina was stable for 8 h post-mortem when eyes were stored at 4 °C, but showed increasing changes thereafter. However, some more rapid changes were observed such as increases in TCA cycle metabolites after 2 h post-mortem, whereas some metabolites such as fatty acids only showed decreases in concentration from 24 h. A total of 42 metabolites were identified across the ocular tissues by GC-MS (MSI level 1) and 2782 metabolites were annotated by UHPLC-MS (MSI level 2) according to MSI reporting standards. Many of the metabolites detected were common to all of the tissues but some metabolites showed partitioning between different ocular structures with 655, 297, 93 and 13 metabolites being uniquely detected in the retina, lens, cornea and vitreous respectively. Only a small percentage (1.6%) of metabolites found in the vitreous were only detected in the retina and not other tissues. In conclusion, mass spectrometry-based techniques have been used for the first time to compare the metabolic composition of different ocular tissues. The metabolite composition of the retina stored at 4 °C post-mortem is mostly stable for at least 8 h. Copyright © 2016 Elsevier Ltd. All rights reserved.

Classification of pulmonary airway disease based on mucosal color analysis

NASA Astrophysics Data System (ADS)

Suter, Melissa; Reinhardt, Joseph M.; Riker, David; Ferguson, John Scott; McLennan, Geoffrey

2005-04-01

Airway mucosal color changes occur in response to the development of bronchial diseases including lung cancer, cystic fibrosis, chronic bronchitis, emphysema and asthma. These associated changes are often visualized using standard macro-optical bronchoscopy techniques. A limitation to this form of assessment is that the subtle changes that indicate early stages in disease development may often be missed as a result of this highly subjective assessment, especially in inexperienced bronchoscopists. Tri-chromatic CCD chip bronchoscopes allow for digital color analysis of the pulmonary airway mucosa. This form of analysis may facilitate a greater understanding of airway disease response. A 2-step image classification approach is employed: the first step is to distinguish between healthy and diseased bronchoscope images and the second is to classify the detected abnormal images into 1 of 4 possible disease categories. A database of airway mucosal color constructed from healthy human volunteers is used as a standard against which statistical comparisons are made from mucosa with known apparent airway abnormalities. This approach demonstrates great promise as an effective detection and diagnosis tool to highlight potentially abnormal airway mucosa identifying a region possibly suited to further analysis via airway forceps biopsy, or newly developed micro-optical biopsy strategies. Following the identification of abnormal airway images a neural network is used to distinguish between the different disease classes. We have shown that classification of potentially diseased airway mucosa is possible through comparative color analysis of digital bronchoscope images. The combination of the two strategies appears to increase the classification accuracy in addition to greatly decreasing the computational time.

Detection of epileptic seizure in EEG signals using linear least squares preprocessing.

PubMed

Roshan Zamir, Z

2016-09-01

An epileptic seizure is a transient event of abnormal excessive neuronal discharge in the brain. This unwanted event can be obstructed by detection of electrical changes in the brain that happen before the seizure takes place. The automatic detection of seizures is necessary since the visual screening of EEG recordings is a time consuming task and requires experts to improve the diagnosis. Much of the prior research in detection of seizures has been developed based on artificial neural network, genetic programming, and wavelet transforms. Although the highest achieved accuracy for classification is 100%, there are drawbacks, such as the existence of unbalanced datasets and the lack of investigations in performances consistency. To address these, four linear least squares-based preprocessing models are proposed to extract key features of an EEG signal in order to detect seizures. The first two models are newly developed. The original signal (EEG) is approximated by a sinusoidal curve. Its amplitude is formed by a polynomial function and compared with the predeveloped spline function. Different statistical measures, namely classification accuracy, true positive and negative rates, false positive and negative rates and precision, are utilised to assess the performance of the proposed models. These metrics are derived from confusion matrices obtained from classifiers. Different classifiers are used over the original dataset and the set of extracted features. The proposed models significantly reduce the dimension of the classification problem and the computational time while the classification accuracy is improved in most cases. The first and third models are promising feature extraction methods with the classification accuracy of 100%. Logistic, LazyIB1, LazyIB5, and J48 are the best classifiers. Their true positive and negative rates are 1 while false positive and negative rates are 0 and the corresponding precision values are 1. Numerical results suggest that these models are robust and efficient for detecting epileptic seizure. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Enhanced change detection index for disaster response, recovery assessment and monitoring of buildings and critical facilities-A case study for Muzzaffarabad, Pakistan

NASA Astrophysics Data System (ADS)

de Alwis Pitts, Dilkushi A.; So, Emily

2017-12-01

The availability of Very High Resolution (VHR) optical sensors and a growing image archive that is frequently updated, allows the use of change detection in post-disaster recovery and monitoring for robust and rapid results. The proposed semi-automated GIS object-based method uses readily available pre-disaster GIS data and adds existing knowledge into the processing to enhance change detection. It also allows targeting specific types of changes pertaining to similar man-made objects such as buildings and critical facilities. The change detection method is based on pre/post normalized index, gradient of intensity, texture and edge similarity filters within the object and a set of training data. More emphasis is put on the building edges to capture the structural damage in quantifying change after disaster. Once the change is quantified, based on training data, the method can be used automatically to detect change in order to observe recovery over time in potentially large areas. Analysis over time can also contribute to obtaining a full picture of the recovery and development after disaster, thereby giving managers a better understanding of productive management and recovery practices. The recovery and monitoring can be analyzed using the index in zones extending from to epicentre of disaster or administrative boundaries over time.

Detecting temporal changes in acoustic scenes: The variable benefit of selective attention.

PubMed

Demany, Laurent; Bayle, Yann; Puginier, Emilie; Semal, Catherine

2017-09-01

Four experiments investigated change detection in acoustic scenes consisting of a sum of five amplitude-modulated pure tones. As the tones were about 0.7 octave apart and were amplitude-modulated with different frequencies (in the range 2-32 Hz), they were perceived as separate streams. Listeners had to detect a change in the frequency (experiments 1 and 2) or the shape (experiments 3 and 4) of the modulation of one of the five tones, in the presence of an informative cue orienting selective attention either before the scene (pre-cue) or after it (post-cue). The changes left intensity unchanged and were not detectable in the spectral (tonotopic) domain. Performance was much better with pre-cues than with post-cues. Thus, change deafness was manifest in the absence of an appropriate focusing of attention when the change occurred, even though the streams and the changes to be detected were acoustically very simple (in contrast to the conditions used in previous demonstrations of change deafness). In one case, the results were consistent with a model based on the assumption that change detection was possible if and only if attention was endogenously focused on a single tone. However, it was also found that changes resulting in a steepening of amplitude rises were to some extent able to draw attention exogenously. Change detection was not markedly facilitated when the change produced a discontinuity in the modulation domain, contrary to what could be expected from the perspective of predictive coding. Copyright © 2017 Elsevier B.V. All rights reserved.

Clinical relevance of the effects of reach-to-grasp training using trunk restraint in individuals with hemiparesis poststroke: A systematic review.

PubMed

Greisberger, Andrea; Aviv, Hanna; Garbade, Sven F; Diermayr, Gudrun

2016-04-28

To evaluate the evidence for, and clinical relevance of, immediate and long-term effects of trunk restraint during reach-to-grasp training poststroke on movement patterns and functional abilities within the framework of the International Classification of Functioning, Disability and Health. PubMed, Web of Science, CINAHL, Embase, PEDro, Cochrane Library (publication dates January 1985 to March 2015). Randomized controlled trials comparing training using trunk restraint with any other exercise training. Data were extracted by one researcher and checked by two other researchers. The Cochrane Collaboration's tool for assessing risk of bias and the Physiotherapy Evidence Database scale were used by two researchers to assess study quality and risk of bias. Eight studies met the inclusion criteria. Five studies found better recovery of movement patterns (trunk displacement, elbow extension, and/or shoulder flexion - body function/structure) at post-test in the experimental compared with the control groups. Functional abilities (activity/participation) improved more in the experimental groups in 3 studies at post-test. Long-term effects were found in one study after 4 weeks. Trunk restraint has immediate and some long-term effects in adults with chronic stroke. However, these effects are not consistently clinically relevant when referring to minimal detectable change or minimal clinically important difference values.

Monitoring wetlands change using LANDSAT data

NASA Technical Reports Server (NTRS)

Hardin, D. L.

1981-01-01

A wetlands monitoring study was initiated as part of Delaware's LANDSAT applications demonstration project. Classifications of digital data are conducted in an effort to determine the location and acreage of wetlands loss or gain, species conversion, and application for the inventory and typing of freshwater wetlands. A multi-seasonal approach is employed to compare data from two different years. Unsupervised classifications were conducted for two of the four dates examined. Initial results indicate the multi-seasonal approach allows much better separation of wetland types for both tidal and non-tidal wetlands than either season alone. Change detection is possible but generally misses the small acreages now impacted by man.

FHR patterns that become significant in connection with ST waveform changes and metabolic acidosis at birth.

PubMed

Rosén, Karl G; Norén, Håkan; Carlsson, Ann

2018-04-18

Recent developments have produced new CTG classification systems and the question is to what extent these may affect the model of FHR + ST interpretation? The two new systems (FIGO2015 and SSOG2017) classify FHR + ST events differently from the current CTG classification system used in the STAN interpretation algorithm (STAN2007). Identify the predominant FHR patterns in connection with ST events in cases of cord artery metabolic acidosis missed by the different CTG classification systems. Indicate to what extent STAN clinical guidelines could be modified enhancing the sensitivity. Provide a pathophysiological rationale. Forty-four cases with umbilical cord artery metabolic acidosis were retrieved from a European multicenter database. Significant FHR + ST events were evaluated post hoc in consensus by an expert panel. Eighteen cases were not identified as in need of intervention and regarded as negative in the sensitivity analysis. In 12 cases, ST changes occurred but the CTG was regarded as reassuring. Visual analysis of the FHR + ST tracings revealed specific FHR patterns: Conclusion: These findings indicate FHR + ST analysis may be undertaken regardless of CTG classification system provided there is a more physiologically oriented approach to FHR assessment in connection with an ST event.

An Auscultaiting Diagnosis Support System for Assessing Hemodialysis Shunt Stenosis by Using Self-organizing Map

NASA Astrophysics Data System (ADS)

Suzuki, Yutaka; Fukasawa, Mizuya; Sakata, Osamu; Kato, Hatsuhiro; Hattori, Asobu; Kato, Takaya

Vascular access for hemodialysis is a lifeline for over 280,000 chronic renal failure patients in Japan. Early detection of stenosis may facilitate long-term use of hemodialysis shunts. Stethoscope auscultation of vascular murmurs has some utility in the assessment of access patency; however, the sensitivity of this diagnostic approach is skill dependent. This study proposes a novel diagnosis support system to detect stenosis by using vascular murmurs. The system is based on a self-organizing map (SOM) and short-time maximum entropy method (STMEM) for data analysis. SOM is an artificial neural network, which is trained using unsupervised learning to produce a feature map that is useful for visualizing the analogous relationship between input data. The author recorded vascular murmurs before and after percutaneous transluminal angioplasty (PTA). The SOM-based classification was consistent with to the classification based on MEM spectral and spectrogram characteristics. The ratio of pre-PTA murmurs in the stenosis category was much higher than the post-PTA murmurs. The results suggest that the proposed method may be an effective tool in the determination of shunt stenosis.

Role of EEG as Biomarker in the Early Detection and Classification of Dementia

PubMed Central

Al-Qazzaz, Noor Kamal; Ali, Sawal Hamid Bin MD.; Ahmad, Siti Anom; Chellappan, Kalaivani; Islam, Md. Shabiul; Escudero, Javier

2014-01-01

The early detection and classification of dementia are important clinical support tasks for medical practitioners in customizing patient treatment programs to better manage the development and progression of these diseases. Efforts are being made to diagnose these neurodegenerative disorders in the early stages. Indeed, early diagnosis helps patients to obtain the maximum treatment benefit before significant mental decline occurs. The use of electroencephalogram as a tool for the detection of changes in brain activities and clinical diagnosis is becoming increasingly popular for its capabilities in quantifying changes in brain degeneration in dementia. This paper reviews the role of electroencephalogram as a biomarker based on signal processing to detect dementia in early stages and classify its severity. The review starts with a discussion of dementia types and cognitive spectrum followed by the presentation of the effective preprocessing denoising to eliminate possible artifacts. It continues with a description of feature extraction by using linear and nonlinear techniques, and it ends with a brief explanation of vast variety of separation techniques to classify EEG signals. This paper also provides an idea from the most popular studies that may help in diagnosing dementia in early stages and classifying through electroencephalogram signal processing and analysis. PMID:25093211

Role of EEG as biomarker in the early detection and classification of dementia.

PubMed

Al-Qazzaz, Noor Kamal; Ali, Sawal Hamid Bin Md; Ahmad, Siti Anom; Chellappan, Kalaivani; Islam, Md Shabiul; Escudero, Javier

2014-01-01

The early detection and classification of dementia are important clinical support tasks for medical practitioners in customizing patient treatment programs to better manage the development and progression of these diseases. Efforts are being made to diagnose these neurodegenerative disorders in the early stages. Indeed, early diagnosis helps patients to obtain the maximum treatment benefit before significant mental decline occurs. The use of electroencephalogram as a tool for the detection of changes in brain activities and clinical diagnosis is becoming increasingly popular for its capabilities in quantifying changes in brain degeneration in dementia. This paper reviews the role of electroencephalogram as a biomarker based on signal processing to detect dementia in early stages and classify its severity. The review starts with a discussion of dementia types and cognitive spectrum followed by the presentation of the effective preprocessing denoising to eliminate possible artifacts. It continues with a description of feature extraction by using linear and nonlinear techniques, and it ends with a brief explanation of vast variety of separation techniques to classify EEG signals. This paper also provides an idea from the most popular studies that may help in diagnosing dementia in early stages and classifying through electroencephalogram signal processing and analysis.

Change detection with heterogeneous data using ecoregional stratification, statistical summaries and a land allocation algorithm

Treesearch

Kathleen M. Bergen; Daniel G. Brown; James F. Rutherford; Eric J. Gustafson

2005-01-01

A ca. 1980 national-scale land-cover classification based on aerial photo interpretation was combined with 2000 AVHRR satellite imagery to derive land cover and land-cover change information for forest, urban, and agriculture categories over a seven-state region in the U.S. To derive useful land-cover change data using a heterogeneous dataset and to validate our...

Applicability of the ICD-11 proposal for PTSD: a comparison of prevalence and comorbidity rates with the DSM-IV PTSD classification in two post-conflict samples

PubMed Central

Stammel, Nadine; Abbing, Eva M.; Heeke, Carina; Knaevelsrud, Christine

2015-01-01

Background The World Health Organization recently proposed significant changes to the posttraumatic stress disorder (PTSD) diagnostic criteria in the 11th edition of the International Classification of Diseases (ICD-11). Objective The present study investigated the impact of these changes in two different post-conflict samples. Method Prevalence and rates of concurrent depression and anxiety, socio-demographic characteristics, and indicators of clinical severity according to ICD-11 in 1,075 Cambodian and 453 Colombian civilians exposed to civil war and genocide were compared to those according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV). Results Results indicated significantly lower prevalence rates under the ICD-11 proposal (8.1% Cambodian sample and 44.4% Colombian sample) compared to the DSM-IV (11.2% Cambodian sample and 55.0% Colombian sample). Participants meeting a PTSD diagnosis only under the ICD-11 proposal had significantly lower rates of concurrent depression and a lower concurrent total score (depression and anxiety) compared to participants meeting only DSM-IV diagnostic criteria. There were no significant differences in socio-demographic characteristics and indicators of clinical severity between these two groups. Conclusions The lower prevalence of PTSD according to the ICD-11 proposal in our samples of persons exposed to a high number of traumatic events may counter criticism of previous PTSD classifications to overuse the PTSD diagnosis in populations exposed to extreme stressors. Also another goal, to better distinguish PTSD from comorbid disorders could be supported with our data. PMID:25989951

Applicability of the ICD-11 proposal for PTSD: a comparison of prevalence and comorbidity rates with the DSM-IV PTSD classification in two post-conflict samples.

PubMed

Stammel, Nadine; Abbing, Eva M; Heeke, Carina; Knaevelsrud, Christine

2015-01-01

The World Health Organization recently proposed significant changes to the posttraumatic stress disorder (PTSD) diagnostic criteria in the 11th edition of the International Classification of Diseases (ICD-11). The present study investigated the impact of these changes in two different post-conflict samples. Prevalence and rates of concurrent depression and anxiety, socio-demographic characteristics, and indicators of clinical severity according to ICD-11 in 1,075 Cambodian and 453 Colombian civilians exposed to civil war and genocide were compared to those according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV). Results indicated significantly lower prevalence rates under the ICD-11 proposal (8.1% Cambodian sample and 44.4% Colombian sample) compared to the DSM-IV (11.2% Cambodian sample and 55.0% Colombian sample). Participants meeting a PTSD diagnosis only under the ICD-11 proposal had significantly lower rates of concurrent depression and a lower concurrent total score (depression and anxiety) compared to participants meeting only DSM-IV diagnostic criteria. There were no significant differences in socio-demographic characteristics and indicators of clinical severity between these two groups. The lower prevalence of PTSD according to the ICD-11 proposal in our samples of persons exposed to a high number of traumatic events may counter criticism of previous PTSD classifications to overuse the PTSD diagnosis in populations exposed to extreme stressors. Also another goal, to better distinguish PTSD from comorbid disorders could be supported with our data.

Developing a semi/automated protocol to post-process large volume, High-resolution airborne thermal infrared (TIR) imagery for urban waste heat mapping

NASA Astrophysics Data System (ADS)

Rahman, Mir Mustafizur

In collaboration with The City of Calgary 2011 Sustainability Direction and as part of the HEAT (Heat Energy Assessment Technologies) project, the focus of this research is to develop a semi/automated 'protocol' to post-process large volumes of high-resolution (H-res) airborne thermal infrared (TIR) imagery to enable accurate urban waste heat mapping. HEAT is a free GeoWeb service, designed to help Calgary residents improve their home energy efficiency by visualizing the amount and location of waste heat leaving their homes and communities, as easily as clicking on their house in Google Maps. HEAT metrics are derived from 43 flight lines of TABI-1800 (Thermal Airborne Broadband Imager) data acquired on May 13--14, 2012 at night (11:00 pm--5:00 am) over The City of Calgary, Alberta (˜825 km 2) at a 50 cm spatial resolution and 0.05°C thermal resolution. At present, the only way to generate a large area, high-spatial resolution TIR scene is to acquire separate airborne flight lines and mosaic them together. However, the ambient sensed temperature within, and between flight lines naturally changes during acquisition (due to varying atmospheric and local micro-climate conditions), resulting in mosaicked images with different temperatures for the same scene components (e.g. roads, buildings), and mosaic join-lines arbitrarily bisect many thousands of homes. In combination these effects result in reduced utility and classification accuracy including, poorly defined HEAT Metrics, inaccurate hotspot detection and raw imagery that are difficult to interpret. In an effort to minimize these effects, three new semi/automated post-processing algorithms (the protocol) are described, which are then used to generate a 43 flight line mosaic of TABI-1800 data from which accurate Calgary waste heat maps and HEAT metrics can be generated. These algorithms (presented as four peer-reviewed papers)---are: (a) Thermal Urban Road Normalization (TURN)---used to mitigate the microclimatic variability within a thermal flight line based on varying road temperatures; (b) Automated Polynomial Relative Radiometric Normalization (RRN)---which mitigates the between flight line radiometric variability; and (c) Object Based Mosaicking (OBM)---which minimizes the geometric distortion along the mosaic edge between each flight line. A modified Emissivity Modulation technique is also described to correct H-res TIR images for emissivity. This combined radiometric and geometric post-processing protocol (i) increases the visual agreement between TABI-1800 flight lines, (ii) improves radiometric agreement within/between flight lines, (iii) produces a visually seamless mosaic, (iv) improves hot-spot detection and landcover classification accuracy, and (v) provides accurate data for thermal-based HEAT energy models. Keywords: Thermal Infrared, Post-Processing, High Spatial Resolution, Airborne, Thermal Urban Road Normalization (TURN), Relative Radiometric Normalization (RRN), Object Based Mosaicking (OBM), TABI-1800, HEAT, and Automation.

Evaluating fuzzy operators of an object-based image analysis for detecting landslides and their changes

NASA Astrophysics Data System (ADS)

Feizizadeh, Bakhtiar; Blaschke, Thomas; Tiede, Dirk; Moghaddam, Mohammad Hossein Rezaei

2017-09-01

This article presents a method of object-based image analysis (OBIA) for landslide delineation and landslide-related change detection from multi-temporal satellite images. It uses both spatial and spectral information on landslides, through spectral analysis, shape analysis, textural measurements using a gray-level co-occurrence matrix (GLCM), and fuzzy logic membership functionality. Following an initial segmentation step, particular combinations of various information layers were investigated to generate objects. This was achieved by applying multi-resolution segmentation to IRS-1D, SPOT-5, and ALOS satellite imagery in sequential steps of feature selection and object classification, and using slope and flow direction derivatives from a digital elevation model together with topographically-oriented gray level co-occurrence matrices. Fuzzy membership values were calculated for 11 different membership functions using 20 landslide objects from a landslide training data. Six fuzzy operators were used for the final classification and the accuracies of the resulting landslide maps were compared. A Fuzzy Synthetic Evaluation (FSE) approach was adapted for validation of the results and for an accuracy assessment using the landslide inventory database. The FSE approach revealed that the AND operator performed best with an accuracy of 93.87% for 2005 and 94.74% for 2011, closely followed by the MEAN Arithmetic operator, while the OR and AND (*) operators yielded relatively low accuracies. An object-based change detection was then applied to monitor landslide-related changes that occurred in northern Iran between 2005 and 2011. Knowledge rules to detect possible landslide-related changes were developed by evaluating all possible landslide-related objects for both time steps.

Automatic adventitious respiratory sound analysis: A systematic review.

PubMed

Pramono, Renard Xaviero Adhi; Bowyer, Stuart; Rodriguez-Villegas, Esther

2017-01-01

Automatic detection or classification of adventitious sounds is useful to assist physicians in diagnosing or monitoring diseases such as asthma, Chronic Obstructive Pulmonary Disease (COPD), and pneumonia. While computerised respiratory sound analysis, specifically for the detection or classification of adventitious sounds, has recently been the focus of an increasing number of studies, a standardised approach and comparison has not been well established. To provide a review of existing algorithms for the detection or classification of adventitious respiratory sounds. This systematic review provides a complete summary of methods used in the literature to give a baseline for future works. A systematic review of English articles published between 1938 and 2016, searched using the Scopus (1938-2016) and IEEExplore (1984-2016) databases. Additional articles were further obtained by references listed in the articles found. Search terms included adventitious sound detection, adventitious sound classification, abnormal respiratory sound detection, abnormal respiratory sound classification, wheeze detection, wheeze classification, crackle detection, crackle classification, rhonchi detection, rhonchi classification, stridor detection, stridor classification, pleural rub detection, pleural rub classification, squawk detection, and squawk classification. Only articles were included that focused on adventitious sound detection or classification, based on respiratory sounds, with performance reported and sufficient information provided to be approximately repeated. Investigators extracted data about the adventitious sound type analysed, approach and level of analysis, instrumentation or data source, location of sensor, amount of data obtained, data management, features, methods, and performance achieved. A total of 77 reports from the literature were included in this review. 55 (71.43%) of the studies focused on wheeze, 40 (51.95%) on crackle, 9 (11.69%) on stridor, 9 (11.69%) on rhonchi, and 18 (23.38%) on other sounds such as pleural rub, squawk, as well as the pathology. Instrumentation used to collect data included microphones, stethoscopes, and accelerometers. Several references obtained data from online repositories or book audio CD companions. Detection or classification methods used varied from empirically determined thresholds to more complex machine learning techniques. Performance reported in the surveyed works were converted to accuracy measures for data synthesis. Direct comparison of the performance of surveyed works cannot be performed as the input data used by each was different. A standard validation method has not been established, resulting in different works using different methods and performance measure definitions. A review of the literature was performed to summarise different analysis approaches, features, and methods used for the analysis. The performance of recent studies showed a high agreement with conventional non-automatic identification. This suggests that automated adventitious sound detection or classification is a promising solution to overcome the limitations of conventional auscultation and to assist in the monitoring of relevant diseases.

Automatic adventitious respiratory sound analysis: A systematic review

PubMed Central

Bowyer, Stuart; Rodriguez-Villegas, Esther

2017-01-01

Background Automatic detection or classification of adventitious sounds is useful to assist physicians in diagnosing or monitoring diseases such as asthma, Chronic Obstructive Pulmonary Disease (COPD), and pneumonia. While computerised respiratory sound analysis, specifically for the detection or classification of adventitious sounds, has recently been the focus of an increasing number of studies, a standardised approach and comparison has not been well established. Objective To provide a review of existing algorithms for the detection or classification of adventitious respiratory sounds. This systematic review provides a complete summary of methods used in the literature to give a baseline for future works. Data sources A systematic review of English articles published between 1938 and 2016, searched using the Scopus (1938-2016) and IEEExplore (1984-2016) databases. Additional articles were further obtained by references listed in the articles found. Search terms included adventitious sound detection, adventitious sound classification, abnormal respiratory sound detection, abnormal respiratory sound classification, wheeze detection, wheeze classification, crackle detection, crackle classification, rhonchi detection, rhonchi classification, stridor detection, stridor classification, pleural rub detection, pleural rub classification, squawk detection, and squawk classification. Study selection Only articles were included that focused on adventitious sound detection or classification, based on respiratory sounds, with performance reported and sufficient information provided to be approximately repeated. Data extraction Investigators extracted data about the adventitious sound type analysed, approach and level of analysis, instrumentation or data source, location of sensor, amount of data obtained, data management, features, methods, and performance achieved. Data synthesis A total of 77 reports from the literature were included in this review. 55 (71.43%) of the studies focused on wheeze, 40 (51.95%) on crackle, 9 (11.69%) on stridor, 9 (11.69%) on rhonchi, and 18 (23.38%) on other sounds such as pleural rub, squawk, as well as the pathology. Instrumentation used to collect data included microphones, stethoscopes, and accelerometers. Several references obtained data from online repositories or book audio CD companions. Detection or classification methods used varied from empirically determined thresholds to more complex machine learning techniques. Performance reported in the surveyed works were converted to accuracy measures for data synthesis. Limitations Direct comparison of the performance of surveyed works cannot be performed as the input data used by each was different. A standard validation method has not been established, resulting in different works using different methods and performance measure definitions. Conclusion A review of the literature was performed to summarise different analysis approaches, features, and methods used for the analysis. The performance of recent studies showed a high agreement with conventional non-automatic identification. This suggests that automated adventitious sound detection or classification is a promising solution to overcome the limitations of conventional auscultation and to assist in the monitoring of relevant diseases. PMID:28552969

A standard lexicon for biodiversity conservation: unified classifications of threats and actions.

PubMed

Salafsky, Nick; Salzer, Daniel; Stattersfield, Alison J; Hilton-Taylor, Craig; Neugarten, Rachel; Butchart, Stuart H M; Collen, Ben; Cox, Neil; Master, Lawrence L; O'Connor, Sheila; Wilkie, David

2008-08-01

An essential foundation of any science is a standard lexicon. Any given conservation project can be described in terms of the biodiversity targets, direct threats, contributing factors at the project site, and the conservation actions that the project team is employing to change the situation. These common elements can be linked in a causal chain, which represents a theory of change about how the conservation actions are intended to bring about desired project outcomes. If project teams want to describe and share their work and learn from one another, they need a standard and precise lexicon to specifically describe each node along this chain. To date, there have been several independent efforts to develop standard classifications for the direct threats that affect biodiversity and the conservation actions required to counteract these threats. Recognizing that it is far more effective to have only one accepted global scheme, we merged these separate efforts into unified classifications of threats and actions, which we present here. Each classification is a hierarchical listing of terms and associated definitions. The classifications are comprehensive and exclusive at the upper levels of the hierarchy, expandable at the lower levels, and simple, consistent, and scalable at all levels. We tested these classifications by applying them post hoc to 1191 threatened bird species and 737 conservation projects. Almost all threats and actions could be assigned to the new classification systems, save for some cases lacking detailed information. Furthermore, the new classification systems provided an improved way of analyzing and comparing information across projects when compared with earlier systems. We believe that widespread adoption of these classifications will help practitioners more systematically identify threats and appropriate actions, managers to more efficiently set priorities and allocate resources, and most important, facilitate cross-project learning and the development of a systematic science of conservation.

Brain tumor classification of microscopy images using deep residual learning

NASA Astrophysics Data System (ADS)

Ishikawa, Yota; Washiya, Kiyotada; Aoki, Kota; Nagahashi, Hiroshi

2016-12-01

The crisis rate of brain tumor is about one point four in ten thousands. In general, cytotechnologists take charge of cytologic diagnosis. However, the number of cytotechnologists who can diagnose brain tumors is not sufficient, because of the necessity of highly specialized skill. Computer-Aided Diagnosis by computational image analysis may dissolve the shortage of experts and support objective pathological examinations. Our purpose is to support a diagnosis from a microscopy image of brain cortex and to identify brain tumor by medical image processing. In this study, we analyze Astrocytes that is a type of glia cell of central nerve system. It is not easy for an expert to discriminate brain tumor correctly since the difference between astrocytes and low grade astrocytoma (tumors formed from Astrocyte) is very slight. In this study, we present a novel method to segment cell regions robustly using BING objectness estimation and to classify brain tumors using deep convolutional neural networks (CNNs) constructed by deep residual learning. BING is a fast object detection method and we use pretrained BING model to detect brain cells. After that, we apply a sequence of post-processing like Voronoi diagram, binarization, watershed transform to obtain fine segmentation. For classification using CNNs, a usual way of data argumentation is applied to brain cells database. Experimental results showed 98.5% accuracy of classification and 98.2% accuracy of segmentation.

Electrophysiological evidence for parallel and serial processing during visual search.

PubMed

Luck, S J; Hillyard, S A

1990-12-01

Event-related potentials were recorded from young adults during a visual search task in order to evaluate parallel and serial models of visual processing in the context of Treisman's feature integration theory. Parallel and serial search strategies were produced by the use of feature-present and feature-absent targets, respectively. In the feature-absent condition, the slopes of the functions relating reaction time and latency of the P3 component to set size were essentially identical, indicating that the longer reaction times observed for larger set sizes can be accounted for solely by changes in stimulus identification and classification time, rather than changes in post-perceptual processing stages. In addition, the amplitude of the P3 wave on target-present trials in this condition increased with set size and was greater when the preceding trial contained a target, whereas P3 activity was minimal on target-absent trials. These effects are consistent with the serial self-terminating search model and appear to contradict parallel processing accounts of attention-demanding visual search performance, at least for a subset of search paradigms. Differences in ERP scalp distributions further suggested that different physiological processes are utilized for the detection of feature presence and absence.

Effects of Weathering on TIR Spectra and Rock Classification

NASA Astrophysics Data System (ADS)

McDowell, M. L.; Hamilton, V. E.; Riley, D.

2006-03-01

Changes in mineralogy due to weathering are detectable in the TIR and cause misclassification of rock types. We survey samples over a range of lithologies and attempt to provide a method of correction for rock identification from weathered spectra.

New baseline environmental assessment of mosquito ecology in northern Haiti during increased urbanization

PubMed Central

Archer, Reginald S.; Alimi, Temitope O.; Arheart, Kristopher K.; Impoinvil, Daniel E.; Oscar, Roland; Fuller, Douglas O.; Qualls, Whitney A.

2015-01-01

The catastrophic 2010 earthquake in Port-au-Prince, Haiti, led to the large-scale displacement of over 2.3 million people, resulting in rapid and unplanned urbanization in northern Haiti. This study evaluated the impact of this unplanned urbanization on mosquito ecology and vector-borne diseases by assessing land use and change patterns. Land-use classification and change detection were carried out on remotely sensed images of the area for 2010 and 2013. Change detection identified areas that went from agricultural, forest, or bare-land pre-earthquake to newly developed and urbanized areas post-earthquake. Areas to be sampled for mosquito larvae were subsequently identified. Mosquito collections comprised five genera and ten species, with the most abundant species being Culex quinquefasciatus 35% (304/876), Aedes albopictus 27% (238/876), and Aedes aegypti 20% (174/876). All three species were more prevalent in urbanized and newly urbanized areas. Anopheles albimanus, the predominate malaria vector, accounted for less than 1% (8/876) of the collection. A set of spectral indices derived from the recently launched Landsat 8 satellite was used as covariates in a species distribution model. The indices were used to produce probability surfaces maps depicting the likelihood of presence of the three most abundant species within 30 m pixels. Our findings suggest that the rapid urbanization following the 2010 earthquake has increased the amount of area with suitable habitats for urban mosquitoes, likely influencing mosquito ecology and posing a major risk of introducing and establishing emerging vector-borne diseases. PMID:26047183

Land Use and Land Cover Change in Guangzhou, China, from 1998 to 2003, Based on Landsat TM /ETM+ Imagery

PubMed Central

Fan, Fenglei; Weng, Qihao; Wang, Yunpeng

2007-01-01

Land use and land cover change is a major issue in global environment change, and is especially significant in rapidly developing regions in the world. With its economic development, population growth, and urbanization, Guangzhou, a major metropolitan in South China, have experienced a dramatic land use and land cover (LULC) change over the past 30 years. Fast LULC change have resulted in degradation of its ecosystems and affected adversely the environment. It is urgently needed to monitor its LULC changes and to analyses the consequences of these changes in order to provide information for policymakers to support sustainable development. This study employed two Landsat TM/ETM+ images in the dry season to detect LULC patterns in 1998 and 2003, and to examine LULC changes during the period from 1998 to 2003. The type, rate, and pattern of the changes among five counties of Guangzhou Municipality were analyzed in details by post-classification method. LULC conversion matrix was produced for each county in order to explore and explain the urban expansion and cropland loss, the most significant types of LULC change. Land use conversion matrixes of five counties were discussed respectively in order to explore and explain the inherence of land use change. The results showed that urban expansion in these five counties kept an even rate of increase, while substantial amount of cropland vanished during the period. It is also noted that the conversion between cropland and orchard land was intensive. Forest land became the main source of new croplands.

Remote sensing of a dynamic sub-arctic peatland reservoir using optical and synthetic aperture radar data

NASA Astrophysics Data System (ADS)

Larter, Jarod Lee

Stephens Lake, Manitoba is an example of a peatland reservoir that has undergone physical changes related to mineral erosion and peatland disintegration processes since its initial impoundment. In this thesis I focused on the processes of peatland upheaval, transport, and disintegration as the primary drivers of dynamic change within the reservoir. The changes related to these processes are most frequent after initial reservoir impoundment and decline over time. They continue to occur over 35 years after initial flooding. I developed a remote sensing approach that employs both optical and microwave sensors for discriminating land (Le. floating peatlands, forested land, and barren land) from open water within the reservoir. High spatial resolution visible and near-infrared (VNIR) optical data obtained from the QuickBird satellite, and synthetic aperture radar (SAR) microwave data obtained from the RADARSAT-1 satellite were implemented. The approach was facilitated with a Geographic Information System (GIS) based validation map for the extraction of optical and SAR pixel data. Each sensor's extracted data set was first analyzed separately using univariate and multivariate statistical methods to determine the discriminant ability of each sensor. The initial analyses were followed by an integrated sensor approach; the development of an image classification model; and a change detection analysis. Results showed excellent (> 95%) classification accuracy using QuickBird satellite image data. Discrimination and classification of studied land cover classes using SAR image texture data resulted in lower overall classification accuracies (˜ 60%). SAR data classification accuracy improved to > 90% when classifying only land and water, demonstrating SAR's utility as a land and water mapping tool. An integrated sensor data approach showed no considerable improvement over the use of optical satellite image data alone. An image classification model was developed that could be used to map both detailed land cover classes and the land and water interface within the reservoir. Change detection analysis over a seven year period indicated that physical changes related to mineral erosion, peatland upheaval, transport, and disintegration, and operational water level variation continue to take place in the reservoir some 35 years after initial flooding. This thesis demonstrates the ability of optical and SAR satellite image remote sensing data sets to be used in an operational context for the routine discrimination of the land and water boundaries within a dynamic peatland reservoir. Future monitoring programs would benefit most from a complementary image acquisition program in which SAR images, known for their acquisition reliability under cloud cover, are acquired along with optical images given their ability to discriminate land cover classes in greater detail.

A scale-invariant change detection method for land use/cover change research

NASA Astrophysics Data System (ADS)

Xing, Jin; Sieber, Renee; Caelli, Terrence

2018-07-01

Land Use/Cover Change (LUCC) detection relies increasingly on comparing remote sensing images with different spatial and spectral scales. Based on scale-invariant image analysis algorithms in computer vision, we propose a scale-invariant LUCC detection method to identify changes from scale heterogeneous images. This method is composed of an entropy-based spatial decomposition, two scale-invariant feature extraction methods, Maximally Stable Extremal Region (MSER) and Scale-Invariant Feature Transformation (SIFT) algorithms, a spatial regression voting method to integrate MSER and SIFT results, a Markov Random Field-based smoothing method, and a support vector machine classification method to assign LUCC labels. We test the scale invariance of our new method with a LUCC case study in Montreal, Canada, 2005-2012. We found that the scale-invariant LUCC detection method provides similar accuracy compared with the resampling-based approach but this method avoids the LUCC distortion incurred by resampling.

Change detection from synthetic aperture radar images based on neighborhood-based ratio and extreme learning machine

NASA Astrophysics Data System (ADS)

Gao, Feng; Dong, Junyu; Li, Bo; Xu, Qizhi; Xie, Cui

2016-10-01

Change detection is of high practical value to hazard assessment, crop growth monitoring, and urban sprawl detection. A synthetic aperture radar (SAR) image is the ideal information source for performing change detection since it is independent of atmospheric and sunlight conditions. Existing SAR image change detection methods usually generate a difference image (DI) first and use clustering methods to classify the pixels of DI into changed class and unchanged class. Some useful information may get lost in the DI generation process. This paper proposed an SAR image change detection method based on neighborhood-based ratio (NR) and extreme learning machine (ELM). NR operator is utilized for obtaining some interested pixels that have high probability of being changed or unchanged. Then, image patches centered at these pixels are generated, and ELM is employed to train a model by using these patches. Finally, pixels in both original SAR images are classified by the pretrained ELM model. The preclassification result and the ELM classification result are combined to form the final change map. The experimental results obtained on three real SAR image datasets and one simulated dataset show that the proposed method is robust to speckle noise and is effective to detect change information among multitemporal SAR images.

Multiscale Region-Level VHR Image Change Detection via Sparse Change Descriptor and Robust Discriminative Dictionary Learning

PubMed Central

Xu, Yuan; Ding, Kun; Huo, Chunlei; Zhong, Zisha; Li, Haichang; Pan, Chunhong

2015-01-01

Very high resolution (VHR) image change detection is challenging due to the low discriminative ability of change feature and the difficulty of change decision in utilizing the multilevel contextual information. Most change feature extraction techniques put emphasis on the change degree description (i.e., in what degree the changes have happened), while they ignore the change pattern description (i.e., how the changes changed), which is of equal importance in characterizing the change signatures. Moreover, the simultaneous consideration of the classification robust to the registration noise and the multiscale region-consistent fusion is often neglected in change decision. To overcome such drawbacks, in this paper, a novel VHR image change detection method is proposed based on sparse change descriptor and robust discriminative dictionary learning. Sparse change descriptor combines the change degree component and the change pattern component, which are encoded by the sparse representation error and the morphological profile feature, respectively. Robust change decision is conducted by multiscale region-consistent fusion, which is implemented by the superpixel-level cosparse representation with robust discriminative dictionary and the conditional random field model. Experimental results confirm the effectiveness of the proposed change detection technique. PMID:25918748

Assessing multi-decadal land-cover – land-use change in two wildlife protected areas in Tanzania using Landsat imagery

PubMed Central

Mtui, Devolent T.; Lepczyk, Christopher A.; Chen, Qi; Miura, Tomoaki; Cox, Linda J.

2017-01-01

Landscape change in and around protected areas is of concern worldwide given the potential impacts of such change on biodiversity. Given such impacts, we sought to understand the extent of changes in different land-cover types at two protected areas, Tarangire and Katavi National Parks in Tanzania, over the past 27 years. Using Maximum Likelihood classification procedures we derived eight land-cover classes from Landsat TM and ETM+ images, including: woody savannah, savannah, grassland, open and closed shrubland, swamp and water, and bare land. We determined the extent and direction of changes for all land-cover classes using a post-classification comparison technique. The results show declines in woody savannah and increases in barren land and swamps inside and outside Tarangire National Park and increases in woody savannah and savannah, and declines of shrubland and grassland inside and outside Katavi National Park. The decrease of woody savannah was partially due to its conversion into grassland and barren land, possibly caused by human encroachment by cultivation and livestock. Based upon these changes, we recommend management actions to prevent detrimental effects on wildlife populations. PMID:28957397

Space Monitoring of urban sprawl

NASA Astrophysics Data System (ADS)

Nole, G.; Lanorte, A.; Murgante, B.; Lasaponara, R.

2012-04-01

Space Monitoring of urban sprawl Gabriele Nolè (1,2), Antonio Lanorte (1), , Beniamino Murgante (2) and Rosa Lasaponara (1) , (1,2) Institute of Methodologies for Environmental Analysis, National Research Council, Italy (2) Laboratory of Urban and Territorial Systems, University of Basilicata, During the last few decades, in many regions throughout the world abandonment of agricultural land has induced a high concentration of people in densely populated urban areas. The deep social, economic and environmental changes have caused strong and extensive land cover changes. This is regarded as a pressing issue that calls for a clear understanding of the ongoing trends and future urban expansion. The main issue of great importance in modelling urban growth includes spatial and temporal dynamics, scale dynamics, man-induced land use changes. Although urban growth is perceived as necessary for a sustainable economy, uncontrolled or sprawling urban growth can cause various problems, such as, the loss of open space, landscape alteration, environmental pollution, traffic congestion, infrastructure pressure, and other social and economical issues. To face these drawbacks, a continuous monitoring of the urban growth evolution in terms of type and extent of changes over time are essential for supporting planners and decision makers in future urban planning. A critical point for the understanding and monitoring urban expansion processes is the availability of both (i) time-series data set and (ii) updated information relating to the current urban spatial structure a to define and locate the evolution trends. In such a context, an effective contribution can be offered by satellite remote sensing technologies, which are able to provide both historical data archive and up-to-date imagery. Satellite technologies represent a cost-effective mean for obtaining useful data that can be easily and systematically updated for the whole globe. Nowadays medium resolution satellite images, such as Landsat TM or ASTER can be downloaded free of charge from the NASA web site. The use of satellite imagery along with robust data analysis techniques can be used for the monitoring and planning purposes as these enable the reporting of ongoing trends of urban growth at a detailed level. Nevertheless, the exploitation of satellite Earth Observation in the field of the urban growth monitoring is a relatively new tool, although during the last three decades great efforts have been addressed to the application of remote sensing in detecting land use and land cover changes using a number of data analyses, such as: (i) Spectral enhancement based on vegetation index differencing, principal component analysis, Image differencing and visual interpretation and/or classification, (ii) post-classification change differencing and a combination of image enhancement and post-classification comparison, (iii) mixture analysis, (iv) artificial neural networks, (v) landscape metrics (patchiness and map density) and (vi) the integration of geographical information system and remote sensing data. In this paper a comparison of the methods listed before is carried out using satellite time series made up of Landsat MSS, TM, ETM+ASTER for some test areas selected in South of Italy and Cairo in order to extract and quantify urban sprawl and its spatial and temporal feature patterns.

Proposals for new standardized general diagnostic criteria for the secondary headaches.

PubMed

Olesen, J; Steiner, T; Bousser, M-G; Diener, H-C; Dodick, D; First, M B; Goadsby, P J; Göbel, H; Lainez, M J A; Lipton, R B; Nappi, G; Sakai, F; Schoenen, J; Silberstein, S D

2009-12-01

Headache classification is a dynamic process through clinical testing and re-testing of current and proposed criteria. After publication of the second edition of the International Classification of Headache Disorders (ICHD-II), need arose for revisions in the classification of medication overuse headache and chronic migraine. These changes made apparent a further need for broader revisions to the standard formulation of diagnostic criteria for the secondary headaches. Currently, the fourth criterion makes impossible the definitive diagnosis of a secondary headache until the underlying cause has resolved or been cured or greatly ameliorated by therapy, at which time the headache may no longer be present. Given that the main purpose of diagnostic criteria is to enable a diagnosis at the onset of a disease in order to guide treatment, this is unhelpful in clinical practice. In the present paper we propose maintaining a standard approach to the secondary headaches using a set of four criteria A, B, C and D, but we construct these so that the requirement for resolution or successful treatment is removed. The proposal for general diagnostic criteria for the secondary headaches will be entered into the internet-based version of the appendix of ICHD-II. During 2009 the Classification Committee will apply the general criteria to all the specific types of secondary headaches. These, and other changes, will be included in a revision of the entire classification entitled ICHD-IIR, expected to be published in 2010. ICHD-IIR will be printed and posted on the website and will be the official classification of the International Headache Society. Unfortunately, it will be necessary to translate ICHD-IIR into the many languages of the world, but the good news is that no major changes to the headache classification are then foreseen for the next 10 years. Until the printing of ICHD-IIR, the printed ICHD-II criteria remain in place for all other purposes. We issue a plea to the headache community to use and study these proposed general criteria for the secondary headaches in order to provide more evidence for their utility-before their incorporation in the main body of the classification.

Detection of land-use and land cover changes in Franklin, Gulf, and Liberty Counties, Florida, with multitemporal landsat thematic mapper images

Treesearch

Shufen Pan; Guiying Li

2007-01-01

Florida Panhandle region has been experiencing rapid land transformation in the recent decades. To quantify land use and land-cover (LULC) changes and other landscape changes in this area, three counties including Franklin, Liberty and Gulf were taken as a case study and an unsupervised classification approach implemented to Landsat TM images acquired from 1985 to 2005...

The fate of object memory traces under change detection and change blindness.

PubMed

Busch, Niko A

2013-07-03

Observers often fail to detect substantial changes in a visual scene. This so-called change blindness is often taken as evidence that visual representations are sparse and volatile. This notion rests on the assumption that the failure to detect a change implies that representations of the changing objects are lost all together. However, recent evidence suggests that under change blindness, object memory representations may be formed and stored, but not retrieved. This study investigated the fate of object memory representations when changes go unnoticed. Participants were presented with scenes consisting of real world objects, one of which changed on each trial, while recording event-related potentials (ERPs). Participants were first asked to localize where the change had occurred. In an additional recognition task, participants then discriminated old objects, either from the pre-change or the post-change scene, from entirely new objects. Neural traces of object memories were studied by comparing ERPs for old and novel objects. Participants performed poorly in the detection task and often failed to recognize objects from the scene, especially pre-change objects. However, a robust old/novel effect was observed in the ERP, even when participants were change blind and did not recognize the old object. This implicit memory trace was found both for pre-change and post-change objects. These findings suggest that object memories are stored even under change blindness. Thus, visual representations may not be as sparse and volatile as previously thought. Rather, change blindness may point to a failure to retrieve and use these representations for change detection. Copyright © 2013 Elsevier B.V. All rights reserved.

A new early cognitive screening measure to detect cognitive side-effects of electroconvulsive therapy?

PubMed

Martin, Donel M; Katalinic, Natalie; Ingram, Anna; Schweitzer, Isaac; Smith, Deidre J; Hadzi-Pavlovic, Dusan; Loo, Colleen K

2013-12-01

Cognitive side-effects from electroconvulsive therapy (ECT) can be distressing for patients and early detection may have an important role in guiding treatment decisions over the ECT course. This prospective study examined the utility of an early cognitive screening battery for predicting cognitive side-effects which develop later in the ECT course. The screening battery, together with the Mini Mental Status Examination (MMSE), was administered to 123 patients at baseline and after 3 ECT treatments. A more detailed cognitive battery was administered at baseline, after six treatments (post ECT 6) and after the last ECT treatment (post treatment) to assess cognitive side-effects across several domains: global cognition, anterograde memory, executive function, speed and concentration, and retrograde memory. Multivariate analyses examined the predictive utility of change on items from the screening battery for later cognitive changes at post ECT 6 and post treatment. Results showed that changes on a combination of items from the screening battery were predictive of later cognitive changes at post treatment, particularly for anterograde memory (p < 0.01), after controlling for patient and treatment factors. Change on the MMSE predicted cognitive changes at post ECT 6 but not at post treatment. A scoring method for the new screening battery was tested for discriminative ability in a sub-sample of patients. This study provides preliminary evidence that a simple and easy-to-administer measure may potentially be used to help guide clinical treatment decisions to optimise efficacy and cognitive outcomes. Further development of this measure and validation in a more representative ECT clinical population is required. Copyright © 2013 Elsevier Ltd. All rights reserved.

Developing New Coastal Forest Restoration Products Based on Landsat, ASTER, and MODIS Data

NASA Technical Reports Server (NTRS)

Spruce, Joseph P.; Graham, William; Smoot, James

2009-01-01

This paper discusses an ongoing effort to develop new geospatial information products for aiding coastal forest restoration and conservation efforts in coastal Louisiana and Mississippi. This project employs Landsat, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data in conjunction with airborne elevation data to compute coastal forest cover type maps and change detection products. Improved forest mapping products are needed to aid coastal forest restoration and management efforts of State and Federal agencies in the Northern Gulf of Mexico (NGOM) region. In particular, such products may aid coastal forest land acquisition and conservation easement procurements. This region's forests are often disturbed and subjected to multiple biotic and abiotic threats, including subsidence, salt water intrusion, hurricanes, sea-level rise, insect-induced defoliation and mortality, altered hydrology, wildfire, and conversion to non-forest land use. In some cases, such forest disturbance has led to forest loss or loss of regeneration capacity. In response, a case study was conducted to assess and demonstrate the potential of satellite remote sensing products for improving forest type maps and for assessing forest change over the last 25 years. Change detection products are needed for assessing risks for specific priority coastal forest types, such as live oak and baldcypress-dominated forest. Preliminary results indicate Landsat time series data are capable of generating the needed forest type and change detection products. Useful classifications were obtained using 2 strategies: 1) general forest classification based on use of 3 seasons of Landsat data from the same year; and 2) classification of specific forest types of concern using a single date of Landsat data in which a given targeted type is spectrally distinct compared to adjacent forested cover. When available, ASTER data was useful as a complement to Landsat data. Elevation data helped to define areas in which targeted forest types occur, such as live oak forests on natural levees. MODIS Normalized Difference Vegetation Index time series data aided visual assessments of coastal forest damage and recovery from hurricanes. Landsat change detection products enabled change to be identified at the stand level and at 10- year intervals with the earliest date preceding available change detection products from the National Oceanic and Atmospheric Administration and from the U.S. Geological Survey. Additional work is being done in collaboration with State and Federal agency partners in a follow-on NASA ROSES project to refine and validate these new, promising products. The products from the ROSES project will be available for aiding NGOM coastal forest restoration and conservation.

[The clinical classification of acute otitis media with special reference to tympanometry].

PubMed

Subbotina, M V

We have developed a new clinical classification of acute otitis media (AOM) based on the previously proposed classifications of V.T. Palchun with co-workers (1997) and J. Jeger (1970) in which the letter near the stage of the pathological process roughly corresponds to the type of the tympanogram as follows: stage I (acute tubootitis): A, B, C; stage II (acute catarrhal otitis media): A, B, C; stage III (acute purulent otitis media, perforation stage); stage IV (acute purulent otitis media, post-perforation stage); stage V (resolution of otitis media): A - convalescence or recovery, B1 - exudate present in the tympanic cavity; B2 - persisting perforation; C - block of the auditory tube, O - the development of complications. This classification implies the necessity of tympanometry at the stage of diagnostics of AOM although it is not mandatory because the detection of exudate as a result of paracentesis at any of the stages of otitis media will allow to designate the stage of otitis either by letter A, B or C. The application of the new classification described in this article permits to more accurately than before determine the character of the pathological process in the middle ear during the course of acute otitis media which is of special importance in the clinical pediatric practice for the timely and adequate treatment of the children.

Posterior medial meniscus root ligament lesions: MRI classification and associated findings.

PubMed

Choi, Ja-Young; Chang, Eric Y; Cunha, Guilherme M; Tafur, Monica; Statum, Sheronda; Chung, Christine B

2014-12-01

The purposes of this study were to determine the prevalence of altered MRI appearances of "posterior medial meniscus root ligament (PMMRL)" lesions, introduce a classification of lesion types, and report associated findings. We retrospectively reviewed 419 knee MRI studies to identify the presence of PMMRL lesions. Classification was established on the basis of lesions encountered. The medial compartment was assessed for medial meniscal tears in the meniscus proper, medial meniscal extrusion, insertional PMMRL osseous changes, regional synovitis, osteoarthritis, insufficiency fracture, and cruciate ligament abnormality. PMMRL abnormalities occurred in 28.6% (120/419) of the studies: degeneration, 14.3% (60/419) and tear, 14.3% (60/419). Our classification system included degeneration and tearing. Tearing was categorized as partial or complete with delineation of the point of failure as entheseal, midsubstance, or junction to meniscus. Of all tears, 93.3% (56/60) occurred at the meniscal junction. Univariate analysis revealed significant differences between the knees with and without PMMRL lesions in age, medial meniscal tear, medial meniscal extrusion, insertional PMMRL osseous change, regional synovitis, osteoarthritis, insufficiency fracture (p=0.017), and cruciate ligament degeneration (p<0.001). PMMRL lesions are commonly detected in symptomatic patients. We have introduced an MRI classification system. PMMRL lesions are significantly associated with age, medial meniscal tears, medial meniscal extrusion, insertional PMMRL osseous change, regional synovitis, osteoarthritis, insufficiency fracture, and cruciate ligament degeneration.

a Framework of Change Detection Based on Combined Morphologica Features and Multi-Index Classification

NASA Astrophysics Data System (ADS)

Li, S.; Zhang, S.; Yang, D.

2017-09-01

Remote sensing images are particularly well suited for analysis of land cover change. In this paper, we present a new framework for detection of changing land cover using satellite imagery. Morphological features and a multi-index are used to extract typical objects from the imagery, including vegetation, water, bare land, buildings, and roads. Our method, based on connected domains, is different from traditional methods; it uses image segmentation to extract morphological features, while the enhanced vegetation index (EVI), the differential water index (NDWI) are used to extract vegetation and water, and a fragmentation index is used to the correct extraction results of water. HSV transformation and threshold segmentation extract and remove the effects of shadows on extraction results. Change detection is performed on these results. One of the advantages of the proposed framework is that semantic information is extracted automatically using low-level morphological features and indexes. Another advantage is that the proposed method detects specific types of change without any training samples. A test on ZY-3 images demonstrates that our framework has a promising capability to detect change.

Early morning oedema in patients with primary varicose veins without trophic changes.

PubMed

Rastel, Didier; Allaert, François-André

2016-11-01

Chronic lower limb oedema is one of the complications of superficial or deep chronic venous disorders. It is ranked as "C3"on the CEAP classification. In epidemiological studies, the recognition of oedema is mainly based on clinical signs, and oedema is more easily detected in the second part of the day when it becomes evident. We addressed the question whether oedema is already present in the morning in patients suffering of primary varicose veins without trophic changes. In total, 101 patients with primary varicose veins (C2 and/or C3 stage of the CEAP classification) and 122 controls were enrolled as they appeared in our centre. The consultation time was no later than 6 hours after the patient had woken up. Oedema was detected by pitting test and ultrasound. The mean consultation time lapse was 3.7 ± 1.2 hours after waking-up. Oedema was more frequent in the group of primary varicose veins without trophic changes (36 % compared to 14 % in the control group; p < 0.01). Oedema was mainly detected by ultrasound and far less so by the pitting test. Patients with varicose veins have morning oedema more frequently than patients without varicosis and at a higher rate than in epidemiological studies.

Effect of e-learning program on risk assessment and pressure ulcer classification - A randomized study.

PubMed

Bredesen, Ida Marie; Bjøro, Karen; Gunningberg, Lena; Hofoss, Dag

2016-05-01

Pressure ulcers (PUs) are a problem in health care. Staff competency is paramount to PU prevention. Education is essential to increase skills in pressure ulcer classification and risk assessment. Currently, no pressure ulcer learning programs are available in Norwegian. Develop and test an e-learning program for assessment of pressure ulcer risk and pressure ulcer classification. Forty-four nurses working in acute care hospital wards or nursing homes participated and were assigned randomly into two groups: an e-learning program group (intervention) and a traditional classroom lecture group (control). Data was collected immediately before and after training, and again after three months. The study was conducted at one nursing home and two hospitals between May and December 2012. Accuracy of risk assessment (five patient cases) and pressure ulcer classification (40 photos [normal skin, pressure ulcer categories I-IV] split in two sets) were measured by comparing nurse evaluations in each of the two groups to a pre-established standard based on ratings by experts in pressure ulcer classification and risk assessment. Inter-rater reliability was measured by exact percent agreement and multi-rater Fleiss kappa. A Mann-Whitney U test was used for continuous sum score variables. An e-learning program did not improve Braden subscale scoring. For pressure ulcer classification, however, the intervention group scored significantly higher than the control group on several of the categories in post-test immediately after training. However, after three months there were no significant differences in classification skills between the groups. An e-learning program appears to have a greater effect on the accuracy of pressure ulcer classification than classroom teaching in the short term. For proficiency in Braden scoring, no significant effect of educational methods on learning results was detected. Copyright © 2016 Elsevier Ltd. All rights reserved.

Molecules and dust in the Large Magellanic Cloud: new colour classifications for post-Main-Sequence stars

NASA Astrophysics Data System (ADS)

Markwick-Kemper, Ciska; Leisenring, Jarron; Meixner, Margaret; van Dyk, Schuyler; Szczerba, Ryszard

In the Large Magellanic Cloud (LMC), as in the Milky Way, dust formation predominantly occurs in the circumstellar environments of evolved stars. The process of dust condensation is not fully understood, and investigating the dust condensation sequence in the low metallicity environment of the LMC (about half of the solar metallicity), may yield additional insights in the dust condensation process. Topics to be studied include the final condensation products, the correlation of the condensation sequence with evolutionary status of the star, degree of crystallinity of the silicates and ratio of carbon-rich dust producing stars. The composition and properties of dust are most easily studied using infrared spectroscopy, and using the high sensitivity of the Infrared Spectrograph (IRS) on board of Spitzer, we were able to observe the thermal emission from circumstellar dust of these stars individually. A sample of 63 post-Main-Sequence stars were selected, using their 2MASS/MSX colours (Egan et al. 2001). We aimed to cover all post-Main-Sequence evolutionary stages, to make an inventory of the dust condensation products, while we placed a certain emphasis on oxygen-rich AGB stars with a intermediate mass-loss rate, to study the existence of a threshold mass-loss rate above which crystalline silicates are observed (Kemper et al. 2001). Here we will present the observed spectra, along with their spectral classification. We find that a large fraction of the stars we observed exhibit spectral features of carbon-rich dust and molecules, such as SiC, C2H2 and MgS. In fact we find that many of these stars are previously classified as oxygen-rich AGB stars, or as OH/IR stars based on their NIR/MIR colours (Egan et al. 2001). These colours are determined for Galactic samples, while in the LMC sample, stars with a carbon-rich chemistry in their outflows occupy a much larger region of the various colour-colour diagrams. In addition, a large fraction of the sample show amorphous silicate emission, while amorphous silicate absorption is very rare in our sample, even though it is commonly seen in OH/IR stars in the Galaxy, and we included several stars with similar colours in our sample. Also crystalline silicates are not commonly seen in the spectra of oxygen-rich AGB stars, although we do have a small number of detections. We will improve on the 2MASS and MSX colour classification, and will take the first steps toward a Spitzer/IRAC colour classification. The SAGE project (PI: M. Meixner) will map the entire LMC in all four IRAC bands, and thus numerous new IR point sources will be detected, for which a IRAC colour classification scheme will be very useful to determine their evolutionary stage and circumstellar chemistry.

Topological data analyses and machine learning for detection, classification and characterization of atmospheric rivers

NASA Astrophysics Data System (ADS)

Muszynski, G.; Kashinath, K.; Wehner, M. F.; Prabhat, M.; Kurlin, V.

2017-12-01

We investigate novel approaches to detecting, classifying and characterizing extreme weather events, such as atmospheric rivers (ARs), in large high-dimensional climate datasets. ARs are narrow filaments of concentrated water vapour in the atmosphere that bring much of the precipitation in many mid-latitude regions. The precipitation associated with ARs is also responsible for major flooding events in many coastal regions of the world, including the west coast of the United States and western Europe. In this study we combine ideas from Topological Data Analysis (TDA) with Machine Learning (ML) for detecting, classifying and characterizing extreme weather events, like ARs. TDA is a new field that sits at the interface between topology and computer science, that studies "shape" - hidden topological structure - in raw data. It has been applied successfully in many areas of applied sciences, including complex networks, signal processing and image recognition. Using TDA we provide ARs with a shape characteristic as a new feature descriptor for the task of AR classification. In particular, we track the change in topology in precipitable water (integrated water vapour) fields using the Union-Find algorithm. We use the generated feature descriptors with ML classifiers to establish reliability and classification performance of our approach. We utilize the parallel toolkit for extreme climate events analysis (TECA: Petascale Pattern Recognition for Climate Science, Prabhat et al., Computer Analysis of Images and Patterns, 2015) for comparison (it is assumed that events identified by TECA is ground truth). Preliminary results indicate that our approach brings new insight into the study of ARs and provides quantitative information about the relevance of topological feature descriptors in analyses of a large climate datasets. We illustrate this method on climate model output and NCEP reanalysis datasets. Further, our method outperforms existing methods on detection and classification of ARs. This work illustrates that TDA combined with ML may provide a uniquely powerful approach for detection, classification and characterization of extreme weather phenomena.

Satellite altimetry in sea ice regions - detecting open water for estimating sea surface heights

NASA Astrophysics Data System (ADS)

Müller, Felix L.; Dettmering, Denise; Bosch, Wolfgang

2017-04-01

The Greenland Sea and the Farm Strait are transporting sea ice from the central Arctic ocean southwards. They are covered by a dynamic changing sea ice layer with significant influences on the Earth climate system. Between the sea ice there exist various sized open water areas known as leads, straight lined open water areas, and polynyas exhibiting a circular shape. Identifying these leads by satellite altimetry enables the extraction of sea surface height information. Analyzing the radar echoes, also called waveforms, provides information on the surface backscatter characteristics. For example waveforms reflected by calm water have a very narrow and single-peaked shape. Waveforms reflected by sea ice show more variability due to diffuse scattering. Here we analyze altimeter waveforms from different conventional pulse-limited satellite altimeters to separate open water and sea ice waveforms. An unsupervised classification approach employing partitional clustering algorithms such as K-medoids and memory-based classification methods such as K-nearest neighbor is used. The classification is based on six parameters derived from the waveform's shape, for example the maximum power or the peak's width. The open-water detection is quantitatively compared to SAR images processed while accounting for sea ice motion. The classification results are used to derive information about the temporal evolution of sea ice extent and sea surface heights. They allow to provide evidence on climate change relevant influences as for example Arctic sea level rise due to enhanced melting rates of Greenland's glaciers and an increasing fresh water influx into the Arctic ocean. Additionally, the sea ice cover extent analyzed over a long-time period provides an important indicator for a globally changing climate system.

A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals

PubMed Central

2014-01-01

Background The inter-patient classification schema and the Association for the Advancement of Medical Instrumentation (AAMI) standards are important to the construction and evaluation of automated heartbeat classification systems. The majority of previously proposed methods that take the above two aspects into consideration use the same features and classification method to classify different classes of heartbeats. The performance of the classification system is often unsatisfactory with respect to the ventricular ectopic beat (VEB) and supraventricular ectopic beat (SVEB). Methods Based on the different characteristics of VEB and SVEB, a novel hierarchical heartbeat classification system was constructed. This was done in order to improve the classification performance of these two classes of heartbeats by using different features and classification methods. First, random projection and support vector machine (SVM) ensemble were used to detect VEB. Then, the ratio of the RR interval was compared to a predetermined threshold to detect SVEB. The optimal parameters for the classification models were selected on the training set and used in the independent testing set to assess the final performance of the classification system. Meanwhile, the effect of different lead configurations on the classification results was evaluated. Results Results showed that the performance of this classification system was notably superior to that of other methods. The VEB detection sensitivity was 93.9% with a positive predictive value of 90.9%, and the SVEB detection sensitivity was 91.1% with a positive predictive value of 42.2%. In addition, this classification process was relatively fast. Conclusions A hierarchical heartbeat classification system was proposed based on the inter-patient data division to detect VEB and SVEB. It demonstrated better classification performance than existing methods. It can be regarded as a promising system for detecting VEB and SVEB of unknown patients in clinical practice. PMID:24981916

Shadow detection and removal in RGB VHR images for land use unsupervised classification

NASA Astrophysics Data System (ADS)

Movia, A.; Beinat, A.; Crosilla, F.

2016-09-01

Nowadays, high resolution aerial images are widely available thanks to the diffusion of advanced technologies such as UAVs (Unmanned Aerial Vehicles) and new satellite missions. Although these developments offer new opportunities for accurate land use analysis and change detection, cloud and terrain shadows actually limit benefits and possibilities of modern sensors. Focusing on the problem of shadow detection and removal in VHR color images, the paper proposes new solutions and analyses how they can enhance common unsupervised classification procedures for identifying land use classes related to the CO2 absorption. To this aim, an improved fully automatic procedure has been developed for detecting image shadows using exclusively RGB color information, and avoiding user interaction. Results show a significant accuracy enhancement with respect to similar methods using RGB based indexes. Furthermore, novel solutions derived from Procrustes analysis have been applied to remove shadows and restore brightness in the images. In particular, two methods implementing the so called "anisotropic Procrustes" and the "not-centered oblique Procrustes" algorithms have been developed and compared with the linear correlation correction method based on the Cholesky decomposition. To assess how shadow removal can enhance unsupervised classifications, results obtained with classical methods such as k-means, maximum likelihood, and self-organizing maps, have been compared to each other and with a supervised clustering procedure.

Correspondence between EQ-5D health state classifications and EQ VAS scores.

PubMed

Whynes, David K

2008-11-07

The EQ-5D health-related quality of life instrument comprises a health state classification followed by a health evaluation using a visual analogue scale (VAS). The EQ-5D has been employed frequently in economic evaluations, yet the relationship between the two parts of the instrument remains ill-understood. In this paper, we examine the correspondence between VAS scores and health state classifications for a large sample, and identify variables which contribute to determining the VAS scores independently of the health states as classified. A UK trial of management of low-grade abnormalities detected on screening for cervical pre-cancer (TOMBOLA) provided EQ-5D data for over 3,000 women. Information on distress and multi-dimensional health locus of control had been collected using other instruments. A linear regression model was fitted, with VAS score as the dependent variable. Independent variables comprised EQ-5D health state classifications, distress, locus of control, and socio-demographic characteristics. Equivalent EQ-5D and distress data, collected at twelve months, were available for over 2,000 of the women, enabling us to predict changes in VAS score over time from changes in EQ-5D classification and distress. In addition to EQ-5D health state classification, VAS score was influenced by the subject's perceived locus of control, and by her age, educational attainment, ethnic origin and smoking behaviour. Although the EQ-5D classification includes a distress dimension, the independent measure of distress was an additional determinant of VAS score. Changes in VAS score over time were explained by changes in both EQ-5D severities and distress. Women allocated to the experimental management arm of the trial reported an increase in VAS score, independently of any changes in health state and distress. In this sample, EQ VAS scores were predictable from the EQ-5D health state classification, although there also existed other group variables which contributed systematically and independently towards determining such scores. These variables comprised psychological disposition, socio-demographic factors such as age and education, clinically-important distress, and the clinical intervention itself. ISRCTN34841617.

Use of remote sensing for land use policy formulation

NASA Technical Reports Server (NTRS)

1987-01-01

The overall objectives and strategies of the Center for Remote Sensing remain to provide a center for excellence for multidisciplinary scientific expertise to address land-related global habitability and earth observing systems scientific issues. Specific research projects that were underway during the final contract period include: digital classification of coniferous forest types in Michigan's northern lower peninsula; a physiographic ecosystem approach to remote classification and mapping; land surface change detection and inventory; analysis of radiant temperature data; and development of methodologies to assess possible impacts of man's changes of land surface on meteorological parameters. Significant progress in each of the five project areas has occurred. Summaries on each of the projects are provided.

Standoff detection: distinction of bacteria by hyperspectral laser induced fluorescence

NASA Astrophysics Data System (ADS)

Walter, Arne; Duschek, Frank; Fellner, Lea; Grünewald, Karin M.; Hausmann, Anita; Julich, Sandra; Pargmann, Carsten; Tomaso, Herbert; Handke, Jürgen

2016-05-01

Sensitive detection and rapid identification of hazardous bioorganic material with high sensitivity and specificity are essential topics for defense and security. A single method can hardly cover these requirements. While point sensors allow a highly specific identification, they only provide localized information and are comparatively slow. Laser based standoff systems allow almost real-time detection and classification of potentially hazardous material in a wide area and can provide information on how the aerosol may spread. The coupling of both methods may be a promising solution to optimize the acquisition and identification of hazardous substances. The capability of the outdoor LIF system at DLR Lampoldshausen test facility as an online classification tool has already been demonstrated. Here, we present promising data for further differentiation among bacteria. Bacteria species can express unique fluorescence spectra after excitation at 280 nm and 355 nm. Upon deactivation, the spectral features change depending on the deactivation method.

Direct Quantification of Post-Stress-Rest Left Ventricular Motion and Thickening Changes for Myocardial Perfusion SPECT

PubMed Central

Karimi-Ashtiani, Shahryar; Arsanjani, Reza; Fish, Mathews; Kavanagh, Paul; Germano, Guido; Berman, Daniel; Slomka, Piotr

2012-01-01

Changes in myocardial wall motion and thickening during myocardial perfusion single-photon emission computed tomography (MPS) are typically assessed separately from gated studies to assess for stress induced functional abnormalities. We sought to develop and validate a novel approach for automatic quantification of post-stress-rest myocardial motion and thickening changes (MTC). Methods Endocardial surfaces at the end-diastolic and end-systolic frames for post-stress and rest studies were registered automatically to each other by matching ventricular surfaces. Myocardial MTCs were computed and normal limits of change were determined as the mean and standard deviation for each polar sample. Normal limits were utilized to quantify the MTCs for each map and the accumulated sample values were used for abnormality assessments in segmental regions. A hybrid method was devised by combining the Total Perfusion Deficit (TPD) and MTC for each vessel territory. Normal limits were obtained from 100 subjects with low likelihood (LLK) of coronary artery disease (CAD). For validation, 623 subjects with correlating invasive angiography were studied. All subjects had a stress/rest 99mTc-sestamibi exercise or adenosine test, and all had coronary angiography within 3 months of MPS. All MTC and TPD measurements were derived automatically. The diagnostic accuracy for detection of coronary artery disease for MTC+TPD was compared to TPD alone. Results Segmental normal values for motion change were between −1.3 and −4.1 mm and between −30.1% and −9.8% for thickening change. MTC combined with TPD achieved 61% sensitivity for 3-vessel disease (3VD), 63% for 2-vessel disease (2VD), and 90% for 1-vessel disease (1VD) detection vs. 32% for 3VD (P <0.0001), 53% for 2VD (P < 0.001), and 90% for 1VD (P = 1.0) detection with TPD alone method. The specificity for the combined method was 71% for 3VD, 72% for 2VD, and 47% for 1 VD detection vs. 90% for 3VD (P < 0.0001), 80% for 2VD (P <0.001), and 50% for 1VD detection (P=0.0625) for TPD alone method. The accuracy of 3VD detection by MTC+TPD was higher (69%) than the accuracy of TPD + change in ejection fraction (63%), (P< 0.004). Conclusion We established normal limits and a novel method for computation of regional functional changes between post-stress and rest. Combination of (TPD) with MTC improved the sensitivity for the detection of 3VD and 2VD as compared to TPD alone. PMID:22872739

Stratifying Tropical Fires by Land Cover: Insights into Amazonian Fires, Aerosol Loading, and Regional Deforestation

NASA Technical Reports Server (NTRS)

TenHoeve, J. E.; Remer, L. A.; Jacobson, M. Z.

2010-01-01

This study analyzes changes in the number of fires detected on forest, grass, and transition lands during the 2002-2009 biomass burning seasons using fire detection data and co-located land cover classifications from the Moderate Resolution Imaging Spectroradiometer (MODIS). We find that the total number of detected fires correlates well with MODIS mean aerosol optical depth (AOD) from year to year, in accord with other studies. However, we also show that the ratio of forest to savanna fires varies substantially from year to year. Forest fires have trended downward, on average, since the beginning of 2006 despite a modest increase in 2007. Our study suggests that high particulate matter loading detected in 2007 was likely due to a large number of savanna/agricultural fires that year. Finally, we illustrate that the correlation between annual Brazilian deforestation estimates and MODIS fires is considerably higher when fires are stratified by MODIS-derived land cover classifications.

Thermogram breast cancer prediction approach based on Neutrosophic sets and fuzzy c-means algorithm.

PubMed

Gaber, Tarek; Ismail, Gehad; Anter, Ahmed; Soliman, Mona; Ali, Mona; Semary, Noura; Hassanien, Aboul Ella; Snasel, Vaclav

2015-08-01

The early detection of breast cancer makes many women survive. In this paper, a CAD system classifying breast cancer thermograms to normal and abnormal is proposed. This approach consists of two main phases: automatic segmentation and classification. For the former phase, an improved segmentation approach based on both Neutrosophic sets (NS) and optimized Fast Fuzzy c-mean (F-FCM) algorithm was proposed. Also, post-segmentation process was suggested to segment breast parenchyma (i.e. ROI) from thermogram images. For the classification, different kernel functions of the Support Vector Machine (SVM) were used to classify breast parenchyma into normal or abnormal cases. Using benchmark database, the proposed CAD system was evaluated based on precision, recall, and accuracy as well as a comparison with related work. The experimental results showed that our system would be a very promising step toward automatic diagnosis of breast cancer using thermograms as the accuracy reached 100%.

An iterative approach to optimize change classification in SAR time series data

NASA Astrophysics Data System (ADS)

Boldt, Markus; Thiele, Antje; Schulz, Karsten; Hinz, Stefan

2016-10-01

The detection of changes using remote sensing imagery has become a broad field of research with many approaches for many different applications. Besides the simple detection of changes between at least two images acquired at different times, analyses which aim on the change type or category are at least equally important. In this study, an approach for a semi-automatic classification of change segments is presented. A sparse dataset is considered to ensure the fast and simple applicability for practical issues. The dataset is given by 15 high resolution (HR) TerraSAR-X (TSX) amplitude images acquired over a time period of one year (11/2013 to 11/2014). The scenery contains the airport of Stuttgart (GER) and its surroundings, including urban, rural, and suburban areas. Time series imagery offers the advantage of analyzing the change frequency of selected areas. In this study, the focus is set on the analysis of small-sized high frequently changing regions like parking areas, construction sites and collecting points consisting of high activity (HA) change objects. For each HA change object, suitable features are extracted and a k-means clustering is applied as the categorization step. Resulting clusters are finally compared to a previously introduced knowledge-based class catalogue, which is modified until an optimal class description results. In other words, the subjective understanding of the scenery semantics is optimized by the data given reality. Doing so, an even sparsely dataset containing only amplitude imagery can be evaluated without requiring comprehensive training datasets. Falsely defined classes might be rejected. Furthermore, classes which were defined too coarsely might be divided into sub-classes. Consequently, classes which were initially defined too narrowly might be merged. An optimal classification results when the combination of previously defined key indicators (e.g., number of clusters per class) reaches an optimum.

Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

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

Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.

Neuromimetic machine vision and pattern recognition algorithms are of great interest for landscape characterization and change detection in satellite imagery in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methods to the environmental sciences, using adaptive sparse signal processing combined with machine learning. A Hebbian learning rule is used to build multispectral, multiresolution dictionaries from regional satellite normalized band difference index data. Land cover labels are automatically generated via our CoSA algorithm: Clustering of Sparse Approximations, using a clustering distance metric that combines spectral and spatial textural characteristics tomore » help separate geologic, vegetative, and hydrologie features. We demonstrate our method on example Worldview-2 satellite images of an Arctic region, and use CoSA labels to detect seasonal surface changes. In conclusion, our results suggest that neuroscience-based models are a promising approach to practical pattern recognition and change detection problems in remote sensing.« less

Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

DOE PAGES

Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; ...

2014-10-01

Neuromimetic machine vision and pattern recognition algorithms are of great interest for landscape characterization and change detection in satellite imagery in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methods to the environmental sciences, using adaptive sparse signal processing combined with machine learning. A Hebbian learning rule is used to build multispectral, multiresolution dictionaries from regional satellite normalized band difference index data. Land cover labels are automatically generated via our CoSA algorithm: Clustering of Sparse Approximations, using a clustering distance metric that combines spectral and spatial textural characteristics tomore » help separate geologic, vegetative, and hydrologie features. We demonstrate our method on example Worldview-2 satellite images of an Arctic region, and use CoSA labels to detect seasonal surface changes. In conclusion, our results suggest that neuroscience-based models are a promising approach to practical pattern recognition and change detection problems in remote sensing.« less

(Quickly) Testing the Tester via Path Coverage

NASA Technical Reports Server (NTRS)

Groce, Alex

2009-01-01

The configuration complexity and code size of an automated testing framework may grow to a point that the tester itself becomes a significant software artifact, prone to poor configuration and implementation errors. Unfortunately, testing the tester by using old versions of the software under test (SUT) may be impractical or impossible: test framework changes may have been motivated by interface changes in the tested system, or fault detection may become too expensive in terms of computing time to justify running until errors are detected on older versions of the software. We propose the use of path coverage measures as a "quick and dirty" method for detecting many faults in complex test frameworks. We also note the possibility of using techniques developed to diversify state-space searches in model checking to diversify test focus, and an associated classification of tester changes into focus-changing and non-focus-changing modifications.

Semantic Segmentation and Difference Extraction via Time Series Aerial Video Camera and its Application

NASA Astrophysics Data System (ADS)

Amit, S. N. K.; Saito, S.; Sasaki, S.; Kiyoki, Y.; Aoki, Y.

2015-04-01

Google earth with high-resolution imagery basically takes months to process new images before online updates. It is a time consuming and slow process especially for post-disaster application. The objective of this research is to develop a fast and effective method of updating maps by detecting local differences occurred over different time series; where only region with differences will be updated. In our system, aerial images from Massachusetts's road and building open datasets, Saitama district datasets are used as input images. Semantic segmentation is then applied to input images. Semantic segmentation is a pixel-wise classification of images by implementing deep neural network technique. Deep neural network technique is implemented due to being not only efficient in learning highly discriminative image features such as road, buildings etc., but also partially robust to incomplete and poorly registered target maps. Then, aerial images which contain semantic information are stored as database in 5D world map is set as ground truth images. This system is developed to visualise multimedia data in 5 dimensions; 3 dimensions as spatial dimensions, 1 dimension as temporal dimension, and 1 dimension as degenerated dimensions of semantic and colour combination dimension. Next, ground truth images chosen from database in 5D world map and a new aerial image with same spatial information but different time series are compared via difference extraction method. The map will only update where local changes had occurred. Hence, map updating will be cheaper, faster and more effective especially post-disaster application, by leaving unchanged region and only update changed region.

Multiscale-Driven approach to detecting change in Synthetic Aperture Radar (SAR) imagery

NASA Astrophysics Data System (ADS)

Gens, R.; Hogenson, K.; Ajadi, O. A.; Meyer, F. J.; Myers, A.; Logan, T. A.; Arnoult, K., Jr.

2017-12-01

Detecting changes between Synthetic Aperture Radar (SAR) images can be a useful but challenging exercise. SAR with its all-weather capabilities can be an important resource in identifying and estimating the expanse of events such as flooding, river ice breakup, earthquake damage, oil spills, and forest growth, as it can overcome shortcomings of optical methods related to cloud cover. However, detecting change in SAR imagery can be impeded by many factors including speckle, complex scattering responses, low temporal sampling, and difficulty delineating boundaries. In this presentation we use a change detection method based on a multiscale-driven approach. By using information at different resolution levels, we attempt to obtain more accurate change detection maps in both heterogeneous and homogeneous regions. Integrated within the processing flow are processes that 1) improve classification performance by combining Expectation-Maximization algorithms with mathematical morphology, 2) achieve high accuracy in preserving boundaries using measurement level fusion techniques, and 3) combine modern non-local filtering and 2D-discrete stationary wavelet transform to provide robustness against noise. This multiscale-driven approach to change detection has recently been incorporated into the Alaska Satellite Facility (ASF) Hybrid Pluggable Processing Pipeline (HyP3) using radiometrically terrain corrected SAR images. Examples primarily from natural hazards are presented to illustrate the capabilities and limitations of the change detection method.

Breast cancer in the 21st century: from early detection to new therapies.

PubMed

Merino Bonilla, J A; Torres Tabanera, M; Ros Mendoza, L H

The analysis of the causes that have given rise to a change in tendency in the incidence and mortality rates of breast cancer in the last few decades generates important revelations regarding the role of breast screening, the regular application of adjuvant therapies and the change of risk factors. The benefits of early detection have been accompanied by certain adverse effects, even in terms of an excessive number of prophylactic mastectomies. Recently, several updates have been published on the recommendations in breast cancer screening at an international level. On the other hand, the advances in genomics have made it possible to establish a new molecular classification of breast cancer. Our aim is to present an updated overview of the epidemiological situation of breast cancer, as well as some relevant issues from the point of view of diagnosis, such as molecular classification and different strategies for both population-based and opportunistic screening. Copyright © 2017 SERAM. Publicado por Elsevier España, S.L.U. All rights reserved.

Using trend templates in a neonatal seizure algorithm improves detection of short seizures in a foetal ovine model.

PubMed

Zwanenburg, Alex; Andriessen, Peter; Jellema, Reint K; Niemarkt, Hendrik J; Wolfs, Tim G A M; Kramer, Boris W; Delhaas, Tammo

2015-03-01

Seizures below one minute in duration are difficult to assess correctly using seizure detection algorithms. We aimed to improve neonatal detection algorithm performance for short seizures through the use of trend templates for seizure onset and end. Bipolar EEG were recorded within a transiently asphyxiated ovine model at 0.7 gestational age, a common experimental model for studying brain development in humans of 30-34 weeks of gestation. Transient asphyxia led to electrographic seizures within 6-8 h. A total of 3159 seizures, 2386 shorter than one minute, were annotated in 1976 h-long EEG recordings from 17 foetal lambs. To capture EEG characteristics, five features, sensitive to seizures, were calculated and used to derive trend information. Feature values and trend information were used as input for support vector machine classification and subsequently post-processed. Performance metrics, calculated after post-processing, were compared between analyses with and without employing trend information. Detector performance was assessed after five-fold cross-validation conducted ten times with random splits. The use of trend templates for seizure onset and end in a neonatal seizure detection algorithm significantly improves the correct detection of short seizures using two-channel EEG recordings from 54.3% (52.6-56.1) to 59.5% (58.5-59.9) at FDR 2.0 (median (range); p < 0.001, Wilcoxon signed rank test). Using trend templates might therefore aid in detection of short seizures by EEG monitoring at the NICU.

Low-Level detections of halogenated volatile organic compounds in groundwater: Use in vulnerability assessments

USGS Publications Warehouse

Plummer, Niel; Busenberg, E.; Eberts, S.M.; Bexfield, L.M.; Brown, C.J.; Fahlquist, L.S.; Katz, B.G.; Landon, M.K.

2008-01-01

Concentrations of halogenated volatile organic compounds (VOCs) were determined by gas chromatography (GC) with an electron-capture detector (GC-ECD) and by gas chromatography with mass spectrometry (GC-MS) in 109 groundwater samples from five study areas in the United States. In each case, the untreated water sample was used for drinking-water purposes or was from a monitoring well in an area near a drinking-water source. The minimum detection levels (MDLs) for 25 VOCs that were identified in GC-ECD chromatograms, typically, were two to more than four orders of magnitude below the GC-MS MDLs. At least six halogenated VOCs were detected in all of the water samples analyzed by GC-ECD, although one or more VOCs were detected in only 43% of the water samples analyzed by GC-MS. In nearly all of the samples, VOC concentrations were very low and presented no known health risk. Most of the low-level VOC detections indicated post-1940s recharge, or mixtures of recharge that contained a fraction of post-1940s water. Concentrations of selected halogenated VOCs in groundwater from natural and anthropogenic atmospheric sources were estimated and used to recognize water samples that are being impacted by nonatmospheric sources. A classification is presented to perform vulnerability assessments at the scale of individual wells using the number of halogenated VOC detections and total dissolved VOC concentrations in samples of untreated drinking water. The low-level VOC detections are useful in vulnerability assessments, particularly for samples in which no VOCs are detected by GC-MS analysis.

Detection of tuberculosis patterns in digital photographs of chest X-ray images using Deep Learning: feasibility study.

PubMed

Becker, A S; Blüthgen, C; Phi van, V D; Sekaggya-Wiltshire, C; Castelnuovo, B; Kambugu, A; Fehr, J; Frauenfelder, T

2018-03-01

To evaluate the feasibility of Deep Learning-based detection and classification of pathological patterns in a set of digital photographs of chest X-ray (CXR) images of tuberculosis (TB) patients. In this prospective, observational study, patients with previously diagnosed TB were enrolled. Photographs of their CXRs were taken using a consumer-grade digital still camera. The images were stratified by pathological patterns into classes: cavity, consolidation, effusion, interstitial changes, miliary pattern or normal examination. Image analysis was performed with commercially available Deep Learning software in two steps. Pathological areas were first localised; detected areas were then classified. Detection was assessed using receiver operating characteristics (ROC) analysis, and classification using a confusion matrix. The study cohort was 138 patients with human immunodeficiency virus (HIV) and TB co-infection (median age 34 years, IQR 28-40); 54 patients were female. Localisation of pathological areas was excellent (area under the ROC curve 0.82). The software could perfectly distinguish pleural effusions from intraparenchymal changes. The most frequent misclassifications were consolidations as cavitations, and miliary patterns as interstitial patterns (and vice versa). Deep Learning analysis of CXR photographs is a promising tool. Further efforts are needed to build larger, high-quality data sets to achieve better diagnostic performance.

A Bayesian state-space approach for damage detection and classification

NASA Astrophysics Data System (ADS)

Dzunic, Zoran; Chen, Justin G.; Mobahi, Hossein; Büyüköztürk, Oral; Fisher, John W.

2017-11-01

The problem of automatic damage detection in civil structures is complex and requires a system that can interpret collected sensor data into meaningful information. We apply our recently developed switching Bayesian model for dependency analysis to the problems of damage detection and classification. The model relies on a state-space approach that accounts for noisy measurement processes and missing data, which also infers the statistical temporal dependency between measurement locations signifying the potential flow of information within the structure. A Gibbs sampling algorithm is used to simultaneously infer the latent states, parameters of the state dynamics, the dependence graph, and any changes in behavior. By employing a fully Bayesian approach, we are able to characterize uncertainty in these variables via their posterior distribution and provide probabilistic estimates of the occurrence of damage or a specific damage scenario. We also implement a single class classification method which is more realistic for most real world situations where training data for a damaged structure is not available. We demonstrate the methodology with experimental test data from a laboratory model structure and accelerometer data from a real world structure during different environmental and excitation conditions.

Oil Spill Detection: Past and Future Trends

NASA Astrophysics Data System (ADS)

Topouzelis, Konstantinos; Singha, Suman

2016-08-01

In the last 15 years, the detection of oil spills by satellite means has been moved from experimental to operational. Actually, what is really changed is the satellite image availability. From the late 1990's, in the age of "no data" we have moved forward 15 years to the age of "Sentinels" with an abundance of data. Either large accident related to offshore oil exploration and production activity or illegal discharges from tankers, oil on the sea surface is or can be now regularly monitored, over European Waters. National and transnational organizations (i.e. European Maritime Safety Agency's 'CleanSeaNet' Service) are routinely using SAR imagery to detect oil due to it's all weather, day and night imaging capability. However, all these years the scientific methodology on the detection remains relatively constant. From manual analysis to fully automatic detection methodologies, no significant contribution has been published in the last years and certainly none has dramatically changed the rules of the detection. On the contrary, although the overall accuracy of the methodology is questioned, the four main classification steps (dark area detection, features extraction, statistic database creation, and classification) are continuously improving. In recent years, researchers came up with the use of polarimetric SAR data for oil spill detection and characterizations, although utilization of Pol-SAR data for this purpose still remains questionable due to lack of verified dataset and low spatial coverage of Pol-SAR data. The present paper is trying to point out the drawbacks of the oil spill detection in the last years and focus on the bottlenecks of the oil spill detection methodologies. Also, solutions on the basis of data availability, management and analysis are proposed. Moreover, an ideal detection system is discussed regarding satellite image and in situ observations using different scales and sensors.

Remote sensing for wetland mapping and historical change detection at the Nisqually River Delta

USGS Publications Warehouse

Ballanti, Laurel; Byrd, Kristin B.; Woo, Isa; Ellings, Christopher

2017-01-01

Coastal wetlands are important ecosystems for carbon storage and coastal resilience to climate change and sea-level rise. As such, changes in wetland habitat types can also impact ecosystem functions. Our goal was to quantify historical vegetation change within the Nisqually River watershed relevant to carbon storage, wildlife habitat, and wetland sustainability, and identify watershed-scale anthropogenic and hydrodynamic drivers of these changes. To achieve this, we produced time-series classifications of habitat, photosynthetic pathway functional types and species in the Nisqually River Delta for the years 1957, 1980, and 2015. Using an object-oriented approach, we performed a hierarchical classification on historical and current imagery to identify change within the watershed and wetland ecosystems. We found a 188.4 ha (79%) increase in emergent marsh wetland within the Nisqually River Delta between 1957 and 2015 as a result of restoration efforts that occurred in several phases through 2009. Despite these wetland gains, a total of 83.1 ha (35%) of marsh was lost between 1957 and 2015, particularly in areas near the Nisqually River mouth due to erosion and shifting river channels, resulting in a net wetland gain of 105.4 ha (44%). We found the trajectory of wetland recovery coincided with previous studies, demonstrating the role of remote sensing for historical wetland change detection as well as future coastal wetland monitoring.

DIF Trees: Using Classification Trees to Detect Differential Item Functioning

ERIC Educational Resources Information Center

Vaughn, Brandon K.; Wang, Qiu

2010-01-01

A nonparametric tree classification procedure is used to detect differential item functioning for items that are dichotomously scored. Classification trees are shown to be an alternative procedure to detect differential item functioning other than the use of traditional Mantel-Haenszel and logistic regression analysis. A nonparametric…

Social Media Mining for Toxicovigilance: Automatic Monitoring of Prescription Medication Abuse from Twitter.

PubMed

Sarker, Abeed; O'Connor, Karen; Ginn, Rachel; Scotch, Matthew; Smith, Karen; Malone, Dan; Gonzalez, Graciela

2016-03-01

Prescription medication overdose is the fastest growing drug-related problem in the USA. The growing nature of this problem necessitates the implementation of improved monitoring strategies for investigating the prevalence and patterns of abuse of specific medications. Our primary aims were to assess the possibility of utilizing social media as a resource for automatic monitoring of prescription medication abuse and to devise an automatic classification technique that can identify potentially abuse-indicating user posts. We collected Twitter user posts (tweets) associated with three commonly abused medications (Adderall(®), oxycodone, and quetiapine). We manually annotated 6400 tweets mentioning these three medications and a control medication (metformin) that is not the subject of abuse due to its mechanism of action. We performed quantitative and qualitative analyses of the annotated data to determine whether posts on Twitter contain signals of prescription medication abuse. Finally, we designed an automatic supervised classification technique to distinguish posts containing signals of medication abuse from those that do not and assessed the utility of Twitter in investigating patterns of abuse over time. Our analyses show that clear signals of medication abuse can be drawn from Twitter posts and the percentage of tweets containing abuse signals are significantly higher for the three case medications (Adderall(®): 23 %, quetiapine: 5.0 %, oxycodone: 12 %) than the proportion for the control medication (metformin: 0.3 %). Our automatic classification approach achieves 82 % accuracy overall (medication abuse class recall: 0.51, precision: 0.41, F measure: 0.46). To illustrate the utility of automatic classification, we show how the classification data can be used to analyze abuse patterns over time. Our study indicates that social media can be a crucial resource for obtaining abuse-related information for medications, and that automatic approaches involving supervised classification and natural language processing hold promises for essential future monitoring and intervention tasks.

Land-Cover Trends of the Sierra Nevada Ecoregion, 1973-2000

USGS Publications Warehouse

Raumann, Christian G.; Soulard, Christopher E.

2007-01-01

The U.S. Geological Survey has developed and is implementing the Land Cover Trends project to estimate and describe the temporal and spatial distribution and variability of contemporary land-use and land-cover change in the United States. As part of the Land Cover Trends project, the purpose of this study was to assess land-use/land-cover change in the Sierra Nevada ecoregion for the period 1973 to 2000 using a probability sampling technique and satellite imagery. We randomly selected 36 100-km2 sample blocks to derive thematic images of land-use/land-cover for five dates of Landsat imagery (1973, 1980, 1986, 1992, 2000). We visually interpreted as many as 11 land-use/land-cover classes using a 60-meter minimum mapping unit from the five dates of imagery yielding four periods for analysis. Change-detection results from post-classification comparison of our mapped data showed that landscape disturbance from fire was the dominant change from 1973-2000. The second most-common change was forest disturbance resulting from harvest of timber resources by way of clear-cutting. The rates of forest regeneration from temporary fire and harvest disturbances coincided with the rates of disturbance from the previous period. Relatively minor landscape changes were caused by new development and reservoir drawdown. Multiple linear regression analysis suggests that land ownership and the proportion of forest and developed cover types were significant determinants of the likelihood of direct human-induced change occurring in sampling units. Driving forces of change include land ownership, land management such as fire suppression policy, and demand for natural resources.

Metacognitive Confidence Increases with, but Does Not Determine, Visual Perceptual Learning.

PubMed

Zizlsperger, Leopold; Kümmel, Florian; Haarmeier, Thomas

2016-01-01

While perceptual learning increases objective sensitivity, the effects on the constant interaction of the process of perception and its metacognitive evaluation have been rarely investigated. Visual perception has been described as a process of probabilistic inference featuring metacognitive evaluations of choice certainty. For visual motion perception in healthy, naive human subjects here we show that perceptual sensitivity and confidence in it increased with training. The metacognitive sensitivity-estimated from certainty ratings by a bias-free signal detection theoretic approach-in contrast, did not. Concomitant 3Hz transcranial alternating current stimulation (tACS) was applied in compliance with previous findings on effective high-low cross-frequency coupling subserving signal detection. While perceptual accuracy and confidence in it improved with training, there were no statistically significant tACS effects. Neither metacognitive sensitivity in distinguishing between their own correct and incorrect stimulus classifications, nor decision confidence itself determined the subjects' visual perceptual learning. Improvements of objective performance and the metacognitive confidence in it were rather determined by the perceptual sensitivity at the outset of the experiment. Post-decision certainty in visual perceptual learning was neither independent of objective performance, nor requisite for changes in sensitivity, but rather covaried with objective performance. The exact functional role of metacognitive confidence in human visual perception has yet to be determined.

Rapid Target Detection in High Resolution Remote Sensing Images Using Yolo Model

NASA Astrophysics Data System (ADS)

Wu, Z.; Chen, X.; Gao, Y.; Li, Y.

2018-04-01

Object detection in high resolution remote sensing images is a fundamental and challenging problem in the field of remote sensing imagery analysis for civil and military application due to the complex neighboring environments, which can cause the recognition algorithms to mistake irrelevant ground objects for target objects. Deep Convolution Neural Network(DCNN) is the hotspot in object detection for its powerful ability of feature extraction and has achieved state-of-the-art results in Computer Vision. Common pipeline of object detection based on DCNN consists of region proposal, CNN feature extraction, region classification and post processing. YOLO model frames object detection as a regression problem, using a single CNN predicts bounding boxes and class probabilities in an end-to-end way and make the predict faster. In this paper, a YOLO based model is used for object detection in high resolution sensing images. The experiments on NWPU VHR-10 dataset and our airport/airplane dataset gain from GoogleEarth show that, compare with the common pipeline, the proposed model speeds up the detection process and have good accuracy.

Mesopredators in the Blackland Prairie of Texas: Occupancy, detection probability, and diversity in relation to landcover change

NASA Astrophysics Data System (ADS)

Hawkins, Nathan Ryan

The Blackland Prairie of Texas is one of the most heavily altered and threatened ecoregions in North America. A large part of the current threats to the ecoregion are related to rapid urban growth in cities within the Blackland Prairie. Major threats are related to conversion natural and semi-natural land to urban and suburban land, and increased resource demands from the growing urban population. To quantify these changes, I generated landcover maps using unsupervised classification techniques for the years 1984, 1993, 2004, and 2009 in a portion of the central Blackland Prairie centered around Navarro County, Texas and used post-classification comparison to determine the change in landcover. The largest changes in landcover occurred due to the creation of Richland Chambers Reservoir in 1987, and the subsequent flooding of mostly bottomland forest (a loss of 11,858.4ha of water from 1985-1993). This change occurred because of increased demand for water by the population of the Dallas-Fort Worth-Arlington Metropolitan Study Area. Other changes that occurred in the study area included increases in cropland (26,590.14ha from 1985-2009), grass (25,368ha from 1985-2009), and developed (4,877.55ha from 1985-2009) landcover types. Increases in crop and grass landcover types suggest agricultural intensification occurring within the study area, while an increase in developed landcover indicates an increase in urbanization in a mostly rural county. Since changes in landcover have been demonstrated to affect wildlife in other areas, I used motion-sensing camera traps to survey the predator community in the area. I identified species occurring and developed occupancy models using variables that describe a number of landscape features that may be important to those species. Detections were dominated by a few common species, which included coyotes (psi4=0.7778 winter, psi=0.6667 summer), bobcats (psi=0.8889 winter, psi=0.7778 summer), raccoons (psi=1 winter, psi=0.9091 summer), striped skunks (psi=0.9167 winter, psi=0.8182 summer), and opossums (psi=0.9231 winter, psi=0.9231 summer). Uncommon species detected included river otters (psi=0.4000 winter, psi=0.2000 summer), gray foxes (psi=0.2308 winter), and one spotted skunk (psi=0.0833 summer). Results indicate that changes in landscape features will most likely affect mesopredators in the area. Some species will likely increase in abundance and distribution while others will decrease in abundance and distribution as a result of changes. I also estimated species richness using nonparametric richness estimators that use incidence (presence/absence) data. I detected 9 mesopredator species (bobcat, coyote, opossum, river otter, raccoon, striped skunk, domestic dog, and domestic cat, gray fox, and eastern spotted skunk) in the study area. Considering all study sites at once resulted in species richness estimates of 9-10 species, suggesting that most predators were sampled. When study sites were split into eastern and western groups based on habitat gradients within the study area, slightly different estimates were generated. For the western part of the study area, estimates suggest 9-10 species are present, while in the eastern part of the study area, estimates suggest 6-8 species are present. Results suggest that mesopredator richness is declining compared to historical levels. Large contiguous prairie habitats need to be protected and/or restored to conserve rare prairie communities and the mesopredators they support.

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

DTIC Science & Technology

2014-03-27

FULLY AUTOMATED SUNSPOT DETECTION AND CLASSIFICATION USING SDO HMI IMAGERY IN MATLAB THESIS Gordon M. Spahr, Second Lieutenant, USAF AFIT-ENP-14-M-34...CLASSIFICATION USING SDO HMI IMAGERY IN MATLAB THESIS Presented to the Faculty Department of Engineering Physics Graduate School of Engineering and Management Air...DISTRIUBUTION UNLIMITED. AFIT-ENP-14-M-34 FULLY AUTOMATED SUNSPOT DETECTION AND CLASSIFICATION USING SDO HMI IMAGERY IN MATLAB Gordon M. Spahr, BS Second

Local pulmonary structure classification for computer-aided nodule detection

NASA Astrophysics Data System (ADS)

Bahlmann, Claus; Li, Xianlin; Okada, Kazunori

2006-03-01

We propose a new method of classifying the local structure types, such as nodules, vessels, and junctions, in thoracic CT scans. This classification is important in the context of computer aided detection (CAD) of lung nodules. The proposed method can be used as a post-process component of any lung CAD system. In such a scenario, the classification results provide an effective means of removing false positives caused by vessels and junctions thus improving overall performance. As main advantage, the proposed solution transforms the complex problem of classifying various 3D topological structures into much simpler 2D data clustering problem, to which more generic and flexible solutions are available in literature, and which is better suited for visualization. Given a nodule candidate, first, our solution robustly fits an anisotropic Gaussian to the data. The resulting Gaussian center and spread parameters are used to affine-normalize the data domain so as to warp the fitted anisotropic ellipsoid into a fixed-size isotropic sphere. We propose an automatic method to extract a 3D spherical manifold, containing the appropriate bounding surface of the target structure. Scale selection is performed by a data driven entropy minimization approach. The manifold is analyzed for high intensity clusters, corresponding to protruding structures. Techniques involve EMclustering with automatic mode number estimation, directional statistics, and hierarchical clustering with a modified Bhattacharyya distance. The estimated number of high intensity clusters explicitly determines the type of pulmonary structures: nodule (0), attached nodule (1), vessel (2), junction (>3). We show accurate classification results for selected examples in thoracic CT scans. This local procedure is more flexible and efficient than current state of the art and will help to improve the accuracy of general lung CAD systems.

LANDSAT data for coastal zone management. [New Jersey

NASA Technical Reports Server (NTRS)

Mckenzie, S.

1981-01-01

The lack of adequate, current data on land and water surface conditions in New Jersey led to the search for better data collections and analysis techniques. Four-channel MSS data of Cape May County and access to the OSER computer interpretation system were provided by NASA. The spectral resolution of the data was tested and a surface cover map was produced by going through the steps of supervised classification. Topics covered include classification; change detection and improvement of spectral and spatial resolution; merging LANDSAT and map data; and potential applications for New Jersey.

A dual-process account of auditory change detection.

PubMed

McAnally, Ken I; Martin, Russell L; Eramudugolla, Ranmalee; Stuart, Geoffrey W; Irvine, Dexter R F; Mattingley, Jason B

2010-08-01

Listeners can be "deaf" to a substantial change in a scene comprising multiple auditory objects unless their attention has been directed to the changed object. It is unclear whether auditory change detection relies on identification of the objects in pre- and post-change scenes. We compared the rates at which listeners correctly identify changed objects with those predicted by change-detection models based on signal detection theory (SDT) and high-threshold theory (HTT). Detected changes were not identified as accurately as predicted by models based on either theory, suggesting that some changes are detected by a process that does not support change identification. Undetected changes were identified as accurately as predicted by the HTT model but much less accurately than predicted by the SDT models. The process underlying change detection was investigated further by determining receiver-operating characteristics (ROCs). ROCs did not conform to those predicted by either a SDT or a HTT model but were well modeled by a dual-process that incorporated HTT and SDT components. The dual-process model also accurately predicted the rates at which detected and undetected changes were correctly identified.

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.

Improving the extraction of crisis information in the context of flood, fire, and landslide rapid mapping using SAR and optical remote sensing data

NASA Astrophysics Data System (ADS)

Martinis, Sandro; Clandillon, Stephen; Twele, André; Huber, Claire; Plank, Simon; Maxant, Jérôme; Cao, Wenxi; Caspard, Mathilde; May, Stéphane

2016-04-01

Optical and radar satellite remote sensing have proven to provide essential crisis information in case of natural disasters, humanitarian relief activities and civil security issues in a growing number of cases through mechanisms such as the Copernicus Emergency Management Service (EMS) of the European Commission or the International Charter 'Space and Major Disasters'. The aforementioned programs and initiatives make use of satellite-based rapid mapping services aimed at delivering reliable and accurate crisis information after natural hazards. Although these services are increasingly operational, they need to be continuously updated and improved through research and development (R&D) activities. The principal objective of ASAPTERRA (Advancing SAR and Optical Methods for Rapid Mapping), the ESA-funded R&D project being described here, is to improve, automate and, hence, speed-up geo-information extraction procedures in the context of natural hazards response. This is performed through the development, implementation, testing and validation of novel image processing methods using optical and Synthetic Aperture Radar (SAR) data. The methods are mainly developed based on data of the German radar satellites TerraSAR-X and TanDEM-X, the French satellite missions Pléiades-1A/1B as well as the ESA missions Sentinel-1/2 with the aim to better characterize the potential and limitations of these sensors and their synergy. The resulting algorithms and techniques are evaluated in real case applications during rapid mapping activities. The project is focussed on three types of natural hazards: floods, landslides and fires. Within this presentation an overview of the main methodological developments in each topic is given and demonstrated in selected test areas. The following developments are presented in the context of flood mapping: a fully automated Sentinel-1 based processing chain for detecting open flood surfaces, a method for the improved detection of flooded vegetation in Sentinel-1data using Entropy/Alpha decomposition, unsupervised Wishart Classification, and object-based post-classification as well as semi-automatic approaches for extracting inundated areas and flood traces in rural and urban areas from VHR and HR optical imagery using machine learning techniques. Methodological developments related to fires are the implementation of fast and robust methods for mapping burnt scars using change detection procedures using SAR (Sentinel-1, TerraSAR-X) and HR optical (e.g. SPOT, Sentinel-2) data as well as the extraction of 3D surface and volume change information from Pléiades stereo-pairs. In the context of landslides, fast and transferable change detection procedures based on SAR (TerraSAR-X) and optical (SPOT) data as well methods for extracting the extent of landslides only based on polarimetric VHR SAR (TerraSAR-X) data are presented.

The footprints of visual attention in the Posner cueing paradigm revealed by classification images

NASA Technical Reports Server (NTRS)

Eckstein, Miguel P.; Shimozaki, Steven S.; Abbey, Craig K.

2002-01-01

In the Posner cueing paradigm, observers' performance in detecting a target is typically better in trials in which the target is present at the cued location than in trials in which the target appears at the uncued location. This effect can be explained in terms of a Bayesian observer where visual attention simply weights the information differently at the cued (attended) and uncued (unattended) locations without a change in the quality of processing at each location. Alternatively, it could also be explained in terms of visual attention changing the shape of the perceptual filter at the cued location. In this study, we use the classification image technique to compare the human perceptual filters at the cued and uncued locations in a contrast discrimination task. We did not find statistically significant differences between the shapes of the inferred perceptual filters across the two locations, nor did the observed differences account for the measured cueing effects in human observers. Instead, we found a difference in the magnitude of the classification images, supporting the idea that visual attention changes the weighting of information at the cued and uncued location, but does not change the quality of processing at each individual location.

Integrating remote sensing and terrain data in forest fire modeling

NASA Astrophysics Data System (ADS)

Medler, Michael Johns

Forest fire policies are changing. Managers now face conflicting imperatives to re-establish pre-suppression fire regimes, while simultaneously preventing resource destruction. They must, therefore, understand the spatial patterns of fires. Geographers can facilitate this understanding by developing new techniques for mapping fire behavior. This dissertation develops such techniques for mapping recent fires and using these maps to calibrate models of potential fire hazards. In so doing, it features techniques that strive to address the inherent complexity of modeling the combinations of variables found in most ecological systems. Image processing techniques were used to stratify the elements of terrain, slope, elevation, and aspect. These stratification images were used to assure sample placement considered the role of terrain in fire behavior. Examination of multiple stratification images indicated samples were placed representatively across a controlled range of scales. The incorporation of terrain data also improved preliminary fire hazard classification accuracy by 40%, compared with remotely sensed data alone. A Kauth-Thomas transformation (KT) of pre-fire and post-fire Thematic Mapper (TM) remotely sensed data produced brightness, greenness, and wetness images. Image subtraction indicated fire induced change in brightness, greenness, and wetness. Field data guided a fuzzy classification of these change images. Because fuzzy classification can characterize a continuum of a phenomena where discrete classification may produce artificial borders, fuzzy classification was found to offer a range of fire severity information unavailable with discrete classification. These mapped fire patterns were used to calibrate a model of fire hazards for the entire mountain range. Pre-fire TM, and a digital elevation model produced a set of co-registered images. Training statistics were developed from 30 polygons associated with the previously mapped fire severity. Fuzzy classifications of potential burn patterns were produced from these images. Observed field data values were displayed over the hazard imagery to indicate the effectiveness of the model. Areas that burned without suppression during maximum fire severity are predicted best. Areas with widely spaced trees and grassy understory appear to be misrepresented, perhaps as a consequence of inaccuracies in the initial fire mapping.

Intelligent Classification in Huge Heterogeneous Data Sets

DTIC Science & Technology

2015-06-01

Competencies DoD Department of Defense GMTI Ground Moving Target Indicator ISR Intelligence, Surveillance and Reconnaissance NCD Noncoherent Change...Detection OCR Optical Character Recognition PCA Principal Component Analysis SAR Synthetic Aperture Radar SVD Singular Value Decomponsition USPS United States Postal Service 8 Approved for Public Release; Distribution Unlimited.

A Computer-Aided Type-II Fuzzy Image Processing for Diagnosis of Meniscus Tear.

PubMed

Zarandi, M H Fazel; Khadangi, A; Karimi, F; Turksen, I B

2016-12-01

Meniscal tear is one of the prevalent knee disorders among young athletes and the aging population, and requires correct diagnosis and surgical intervention, if necessary. Not only the errors followed by human intervention but also the obstacles of manual meniscal tear detection highlight the need for automatic detection techniques. This paper presents a type-2 fuzzy expert system for meniscal tear diagnosis using PD magnetic resonance images (MRI). The scheme of the proposed type-2 fuzzy image processing model is composed of three distinct modules: Pre-processing, Segmentation, and Classification. λ-nhancement algorithm is used to perform the pre-processing step. For the segmentation step, first, Interval Type-2 Fuzzy C-Means (IT2FCM) is applied to the images, outputs of which are then employed by Interval Type-2 Possibilistic C-Means (IT2PCM) to perform post-processes. Second stage concludes with re-estimation of "η" value to enhance IT2PCM. Finally, a Perceptron neural network with two hidden layers is used for Classification stage. The results of the proposed type-2 expert system have been compared with a well-known segmentation algorithm, approving the superiority of the proposed system in meniscal tear recognition.

Mapping and Change Analysis in Mangrove Forest by Using Landsat Imagery

NASA Astrophysics Data System (ADS)

Dan, T. T.; Chen, C. F.; Chiang, S. H.; Ogawa, S.

2016-06-01

Mangrove is located in the tropical and subtropical regions and brings good services for native people. Mangrove in the world has been lost with a rapid rate. Therefore, monitoring a spatiotemporal distribution of mangrove is thus critical for natural resource management. This research objectives were: (i) to map the current extent of mangrove in the West and Central Africa and in the Sundarbans delta, and (ii) to identify change of mangrove using Landsat data. The data were processed through four main steps: (1) data pre-processing including atmospheric correction and image normalization, (2) image classification using supervised classification approach, (3) accuracy assessment for the classification results, and (4) change detection analysis. Validation was made by comparing the classification results with the ground reference data, which yielded satisfactory agreement with overall accuracy 84.1% and Kappa coefficient of 0.74 in the West and Central Africa and 83.0% and 0.73 in the Sundarbans, respectively. The result shows that mangrove areas have changed significantly. In the West and Central Africa, mangrove loss from 1988 to 2014 was approximately 16.9%, and only 2.5% was recovered or newly planted at the same time, while the overall change of mangrove in the Sundarbans increased approximately by 900 km2 of total mangrove area. Mangrove declined due to deforestation, natural catastrophes deforestation and mangrove rehabilitation programs. The overall efforts in this study demonstrated the effectiveness of the proposed method used for investigating spatiotemporal changes of mangrove and the results could provide planners with invaluable quantitative information for sustainable management of mangrove ecosystems in these regions.

Detecting Water Bodies in LANDSAT8 Oli Image Using Deep Learning

NASA Astrophysics Data System (ADS)

Jiang, W.; He, G.; Long, T.; Ni, Y.

2018-04-01

Water body identifying is critical to climate change, water resources, ecosystem service and hydrological cycle. Multi-layer perceptron(MLP) is the popular and classic method under deep learning framework to detect target and classify image. Therefore, this study adopts this method to identify the water body of Landsat8. To compare the performance of classification, the maximum likelihood and water index are employed for each study area. The classification results are evaluated from accuracy indices and local comparison. Evaluation result shows that multi-layer perceptron(MLP) can achieve better performance than the other two methods. Moreover, the thin water also can be clearly identified by the multi-layer perceptron. The proposed method has the application potential in mapping global scale surface water with multi-source medium-high resolution satellite data.

39 CFR Appendix A to Subpart A of... - Mail Classification Schedule

Code of Federal Regulations, 2011 CFR

2011-07-01

... Agreement Between United States Postal Service and Koninklijke TNT Post BV and TNT Postl pakketservice Benelux BV, collectively “TNT Post” and China Post Group-United States Postal Service Letter Post...

Keypoint Density-Based Region Proposal for Fine-Grained Object Detection and Classification Using Regions with Convolutional Neural Network Features

DTIC Science & Technology

2015-12-15

Keypoint Density-based Region Proposal for Fine-Grained Object Detection and Classification using Regions with Convolutional Neural Network ... Convolutional Neural Networks (CNNs) enable them to outperform conventional techniques on standard object detection and classification tasks, their...detection accuracy and speed on the fine-grained Caltech UCSD bird dataset (Wah et al., 2011). Recently, Convolutional Neural Networks (CNNs), a deep

A Passive Badge Dosimeter for HCL Detection and Measurement - SBIR 90.I (A90-189)

DTIC Science & Technology

1990-10-02

Microencapsulation ; Toxic gas detection; Combustion Products; RA III; ’i6.PRICECOOE SORR OF____PAGOfABSRAC 17. SECURITY CLASSIFICATION It. SECURITY... microencapsulated samples, all of the sample? changed color when exposed to sufficiently high concentrations of Ha vapor. In general, detector sensitivity...correlted with indicator pKa with the highest sensitivity being noted for indicators with pKa- 7.0. The microencapsulated dye/liquid crystal droplets

43 CFR 2461.4 - Changing classifications.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 43 Public Lands: Interior 2 2011-10-01 2011-10-01 false Changing classifications. 2461.4 Section... MANAGEMENT, DEPARTMENT OF THE INTERIOR LAND RESOURCE MANAGEMENT (2000) BUREAU INITIATED CLASSIFICATION SYSTEM Multiple-Use Classification Procedures § 2461.4 Changing classifications. Classifications may be changed...

43 CFR 2461.4 - Changing classifications.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 43 Public Lands: Interior 2 2012-10-01 2012-10-01 false Changing classifications. 2461.4 Section... MANAGEMENT, DEPARTMENT OF THE INTERIOR LAND RESOURCE MANAGEMENT (2000) BUREAU INITIATED CLASSIFICATION SYSTEM Multiple-Use Classification Procedures § 2461.4 Changing classifications. Classifications may be changed...

43 CFR 2461.4 - Changing classifications.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 43 Public Lands: Interior 2 2014-10-01 2014-10-01 false Changing classifications. 2461.4 Section... MANAGEMENT, DEPARTMENT OF THE INTERIOR LAND RESOURCE MANAGEMENT (2000) BUREAU INITIATED CLASSIFICATION SYSTEM Multiple-Use Classification Procedures § 2461.4 Changing classifications. Classifications may be changed...

43 CFR 2461.4 - Changing classifications.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 43 Public Lands: Interior 2 2013-10-01 2013-10-01 false Changing classifications. 2461.4 Section... MANAGEMENT, DEPARTMENT OF THE INTERIOR LAND RESOURCE MANAGEMENT (2000) BUREAU INITIATED CLASSIFICATION SYSTEM Multiple-Use Classification Procedures § 2461.4 Changing classifications. Classifications may be changed...

An automatic graph-based approach for artery/vein classification in retinal images.

PubMed

Dashtbozorg, Behdad; Mendonça, Ana Maria; Campilho, Aurélio

2014-03-01

The classification of retinal vessels into artery/vein (A/V) is an important phase for automating the detection of vascular changes, and for the calculation of characteristic signs associated with several systemic diseases such as diabetes, hypertension, and other cardiovascular conditions. This paper presents an automatic approach for A/V classification based on the analysis of a graph extracted from the retinal vasculature. The proposed method classifies the entire vascular tree deciding on the type of each intersection point (graph nodes) and assigning one of two labels to each vessel segment (graph links). Final classification of a vessel segment as A/V is performed through the combination of the graph-based labeling results with a set of intensity features. The results of this proposed method are compared with manual labeling for three public databases. Accuracy values of 88.3%, 87.4%, and 89.8% are obtained for the images of the INSPIRE-AVR, DRIVE, and VICAVR databases, respectively. These results demonstrate that our method outperforms recent approaches for A/V classification.

77 FR 46950 - Post Office Organization and Administration: Establishment, Classification, and Discontinuance

Federal Register 2010, 2011, 2012, 2013, 2014

2012-08-07

... defines a remotely managed Post Office (RMPO) as a Post Office that offers part-time window service hours... Administrative Post Office. The final rule also defines a part-time Post Office (PTPO) as a Post Office that offers part-time window service hours, is staffed by a Postal Service employee, and reports to a district...

A Classification System to Guide Physical Therapy Management in Huntington Disease: A Case Series.

PubMed

Fritz, Nora E; Busse, Monica; Jones, Karen; Khalil, Hanan; Quinn, Lori

2017-07-01

Individuals with Huntington disease (HD), a rare neurological disease, experience impairments in mobility and cognition throughout their disease course. The Medical Research Council framework provides a schema that can be applied to the development and evaluation of complex interventions, such as those provided by physical therapists. Treatment-based classifications, based on expert consensus and available literature, are helpful in guiding physical therapy management across the stages of HD. Such classifications also contribute to the development and further evaluation of well-defined complex interventions in this highly variable and complex neurodegenerative disease. The purpose of this case series was to illustrate the use of these classifications in the management of 2 individuals with late-stage HD. Two females, 40 and 55 years of age, with late-stage HD participated in this case series. Both experienced progressive declines in ambulatory function and balance as well as falls or fear of falling. Both individuals received daily care in the home for activities of daily living. Physical therapy Treatment-Based Classifications for HD guided the interventions and outcomes. Eight weeks of in-home balance training, strength training, task-specific practice of functional activities including transfers and walking tasks, and family/carer education were provided. Both individuals demonstrated improvements that met or exceeded the established minimal detectible change values for gait speed and Timed Up and Go performance. Both also demonstrated improvements on Berg Balance Scale and Physical Performance Test performance, with 1 of the 2 individuals exceeding the established minimal detectible changes for both tests. Reductions in fall risk were evident in both cases. These cases provide proof-of-principle to support use of treatment-based classifications for physical therapy management in individuals with HD. Traditional classification of early-, mid-, and late-stage disease progression may not reflect patients' true capabilities; those with late-stage HD may be as responsive to interventions as those at an earlier disease stage.Video Abstract available for additional insights from the authors (see Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A172).

Mild traumatic brain injury literature review and proposed changes to classification.

PubMed

Krainin, Benjamin M; Forsten, Robert D; Kotwal, Russ S; Lutz, Robert H; Guskiewicz, Kevin M

2011-01-01

Mild traumatic brain injury (mTBI) reportedly occurs in 8-22% of U.S. servicemembers who conduct combat operations in Afghanistan and Iraq. The current definition for mTBI found in the medical literature, to include the Department of Defense (DoD) and Veterans Administration (VA) clinical practice guidelines is limited by the parameters of loss of consciousness, altered consciousness, or post-traumatic amnesia, and does not account for other constellations of potential symptoms. Although mTBI symptoms typically resolve within seven days, some servicemembers experience symptoms that continue for weeks, months, or years following an injury. Mild TBI is one of few disorders in medicine where a benign and misleading diagnostic classification is bestowed on patients at the time of injury, yet still can be associated with lifelong complications. This article comprehensively reviews the clinical literature over the past 20 years and proposes a new classification for TBI that addresses acute, sub-acute, and chronic phases, and includes neurocognitive, somatic, and psychological symptom presentation. 2011.

Image Change Detection via Ensemble Learning

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

Martin, Benjamin W; Vatsavai, Raju

2013-01-01

The concept of geographic change detection is relevant in many areas. Changes in geography can reveal much information about a particular location. For example, analysis of changes in geography can identify regions of population growth, change in land use, and potential environmental disturbance. A common way to perform change detection is to use a simple method such as differencing to detect regions of change. Though these techniques are simple, often the application of these techniques is very limited. Recently, use of machine learning methods such as neural networks for change detection has been explored with great success. In this work,more » we explore the use of ensemble learning methodologies for detecting changes in bitemporal synthetic aperture radar (SAR) images. Ensemble learning uses a collection of weak machine learning classifiers to create a stronger classifier which has higher accuracy than the individual classifiers in the ensemble. The strength of the ensemble lies in the fact that the individual classifiers in the ensemble create a mixture of experts in which the final classification made by the ensemble classifier is calculated from the outputs of the individual classifiers. Our methodology leverages this aspect of ensemble learning by training collections of weak decision tree based classifiers to identify regions of change in SAR images collected of a region in the Staten Island, New York area during Hurricane Sandy. Preliminary studies show that the ensemble method has approximately 11.5% higher change detection accuracy than an individual classifier.« less

The Land Cover Dynamics and Conversion of Agricultural Land in Northwestern Bangladesh, 1973-2003.

NASA Astrophysics Data System (ADS)

Pervez, M.; Seelan, S. K.; Rundquist, B. C.

2006-05-01

The importance of land cover information describing the nature and extent of land resources and changes over time is increasing; this is especially true in Bangladesh, where land cover is changing rapidly. This paper presents research into the land cover dynamics of northwestern Bangladesh for the period 1973-2003 using Landsat satellite images in combination with field survey data collected in January and February 2005. Land cover maps were produced for eight different years during the study period with an average 73 percent overall classification accuracy. The classification results and post-classification change analysis showed that agriculture is the dominant land cover (occupying 74.5 percent of the study area) and is being reduced at a rate of about 3,000 ha per year. In addition, 6.7 percent of the agricultural land is vulnerable to temporary water logging annually. Despite this loss of agricultural land, irrigated agriculture increased substantially until 2000, but has since declined because of diminishing water availability and uncontrolled extraction of groundwater driven by population pressures and the extended need for food. A good agreement (r = 0.73) was found between increases in irrigated land and the depletion of the shallow groundwater table, a factor affecting widely practiced small-scale irrigation in northwestern Bangladesh. Results quantified the land cover change patterns and the stresses placed on natural resources; additionally, they demonstrated an accurate and economical means to map and analyze changes in land cover over time at a regional scale, which can assist decision makers in land and natural resources management decisions.

A preliminary evaluation of land use mapping and change detection capabilities using an ERTS image covering a portion of the CARETS region

NASA Technical Reports Server (NTRS)

Fitzpatrick, K. A.; Lins, H. F., Jr.

1972-01-01

The author has identified the following significant results. A preliminary study on the capabilities of ERTS data in land use mapping and change detection was carried out in the area around Frederick County, Maryland, which lies in the northwest corner of the Central Atlantic Regional Ecological Test Site. The investigation has revealed that Level 1 (of the Anderson classification system) land use mapping can be performed and that, in some cases, land undergoing change can be identified. Results to date suggest that more work should be done in areas where land use changes are known to exist, in order to establish some form of base for recognizing the spectral signature indicative of change areas.

Feature learning and change feature classification based on deep learning for ternary change detection in SAR images

NASA Astrophysics Data System (ADS)

Gong, Maoguo; Yang, Hailun; Zhang, Puzhao

2017-07-01

Ternary change detection aims to detect changes and group the changes into positive change and negative change. It is of great significance in the joint interpretation of spatial-temporal synthetic aperture radar images. In this study, sparse autoencoder, convolutional neural networks (CNN) and unsupervised clustering are combined to solve ternary change detection problem without any supervison. Firstly, sparse autoencoder is used to transform log-ratio difference image into a suitable feature space for extracting key changes and suppressing outliers and noise. And then the learned features are clustered into three classes, which are taken as the pseudo labels for training a CNN model as change feature classifier. The reliable training samples for CNN are selected from the feature maps learned by sparse autoencoder with certain selection rules. Having training samples and the corresponding pseudo labels, the CNN model can be trained by using back propagation with stochastic gradient descent. During its training procedure, CNN is driven to learn the concept of change, and more powerful model is established to distinguish different types of changes. Unlike the traditional methods, the proposed framework integrates the merits of sparse autoencoder and CNN to learn more robust difference representations and the concept of change for ternary change detection. Experimental results on real datasets validate the effectiveness and superiority of the proposed framework.

Classification methods to detect sleep apnea in adults based on respiratory and oximetry signals: a systematic review.

PubMed

Uddin, M B; Chow, C M; Su, S W

2018-03-26

Sleep apnea (SA), a common sleep disorder, can significantly decrease the quality of life, and is closely associated with major health risks such as cardiovascular disease, sudden death, depression, and hypertension. The normal diagnostic process of SA using polysomnography is costly and time consuming. In addition, the accuracy of different classification methods to detect SA varies with the use of different physiological signals. If an effective, reliable, and accurate classification method is developed, then the diagnosis of SA and its associated treatment will be time-efficient and economical. This study aims to systematically review the literature and present an overview of classification methods to detect SA using respiratory and oximetry signals and address the automated detection approach. Sixty-two included studies revealed the application of single and multiple signals (respiratory and oximetry) for the diagnosis of SA. Both airflow and oxygen saturation signals alone were effective in detecting SA in the case of binary decision-making, whereas multiple signals were good for multi-class detection. In addition, some machine learning methods were superior to the other classification methods for SA detection using respiratory and oximetry signals. To deal with the respiratory and oximetry signals, a good choice of classification method as well as the consideration of associated factors would result in high accuracy in the detection of SA. An accurate classification method should provide a high detection rate with an automated (independent of human action) analysis of respiratory and oximetry signals. Future high-quality automated studies using large samples of data from multiple patient groups or record batches are recommended.

[Study on biopharmaceutics classification system for Chinese materia medica of extract of Huanglian].

PubMed

Liu, Yang; Yin, Xiu-Wen; Wang, Zi-Yu; Li, Xue-Lian; Pan, Meng; Li, Yan-Ping; Dong, Ling

2017-11-01

One of the advantages of biopharmaceutics classification system of Chinese materia medica (CMMBCS) is expanding the classification research level from single ingredient to multi-components of Chinese herb, and from multi-components research to holistic research of the Chinese materia medica. In present paper, the alkaloids of extract of huanglian were chosen as the main research object to explore their change rules in solubility and intestinal permeability of single-component and multi-components, and to determine the biopharmaceutical classification of extract of Huanglian from holistic level. The typical shake-flask method and HPLC were used to detect the solubility of single ingredient of alkaloids from extract of huanglian. The quantitative research of alkaloids in intestinal absorption was measured in single-pass intestinal perfusion experiment while permeability coefficient of extract of huanglian was calculated by self-defined weight coefficient method. Copyright© by the Chinese Pharmaceutical Association.

Thermal bioaerosol cloud tracking with Bayesian classification

NASA Astrophysics Data System (ADS)

Smith, Christian W.; Dupuis, Julia R.; Schundler, Elizabeth C.; Marinelli, William J.

2017-05-01

The development of a wide area, bioaerosol early warning capability employing existing uncooled thermal imaging systems used for persistent perimeter surveillance is discussed. The capability exploits thermal imagers with other available data streams including meteorological data and employs a recursive Bayesian classifier to detect, track, and classify observed thermal objects with attributes consistent with a bioaerosol plume. Target detection is achieved based on similarity to a phenomenological model which predicts the scene-dependent thermal signature of bioaerosol plumes. Change detection in thermal sensor data is combined with local meteorological data to locate targets with the appropriate thermal characteristics. Target motion is tracked utilizing a Kalman filter and nearly constant velocity motion model for cloud state estimation. Track management is performed using a logic-based upkeep system, and data association is accomplished using a combinatorial optimization technique. Bioaerosol threat classification is determined using a recursive Bayesian classifier to quantify the threat probability of each tracked object. The classifier can accept additional inputs from visible imagers, acoustic sensors, and point biological sensors to improve classification confidence. This capability was successfully demonstrated for bioaerosol simulant releases during field testing at Dugway Proving Grounds. Standoff detection at a range of 700m was achieved for as little as 500g of anthrax simulant. Developmental test results will be reviewed for a range of simulant releases, and future development and transition plans for the bioaerosol early warning platform will be discussed.

19 CFR 152.16 - Judicial changes in classification.

Code of Federal Regulations, 2010 CFR

2010-04-01

... OF THE TREASURY (CONTINUED) CLASSIFICATION AND APPRAISEMENT OF MERCHANDISE Classification § 152.16 Judicial changes in classification. The following procedures apply to changes in classification made by... 19 Customs Duties 2 2010-04-01 2010-04-01 false Judicial changes in classification. 152.16 Section...

Utilizing the Landsat spectral-temporal domain for improved mapping and monitoring of ecosystem state and dynamics

NASA Astrophysics Data System (ADS)

Pasquarella, Valerie J.

Just as the carbon dioxide observations that form the Keeling curve revolutionized the study of the global carbon cycle, free and open access to all available Landsat imagery is fundamentally changing how the Landsat record is being used to study ecosystems and ecological dynamics. This dissertation advances the use of Landsat time series for visualization, classification, and detection of changes in terrestrial ecological processes. More specifically, it includes new examples of how complex ecological patterns manifest in time series of Landsat observations, as well as novel approaches for detecting and quantifying these patterns. Exploration of the complexity of spectral-temporal patterns in the Landsat record reveals both seasonal variability and longer-term trajectories difficult to characterize using conventional bi-temporal or even annual observations. These examples provide empirical evidence of hypothetical ecosystem response functions proposed by Kennedy et al. (2014). Quantifying observed seasonal and phenological differences in the spectral reflectance of Massachusetts' forest communities by combining existing harmonic curve fitting and phenology detection algorithms produces stable feature sets that consistently out-performed more traditional approaches for detailed forest type classification. This study addresses the current lack of species-level forest data at Landsat resolutions, demonstrating the advantages of spectral-temporal features as classification inputs. Development of a targeted change detection method using transformations of time series data improves spatial and temporal information on the occurrence of flood events in landscapes actively modified by recovering North American beaver (Castor canadensis) populations. These results indicate the utility of the Landsat record for the study of species-habitat relationships, even in complex wetland environments. Overall, this dissertation confirms the value of the Landsat archive as a continuous record of terrestrial ecosystem state and dynamics. Given the global coverage of remote sensing datasets, the time series visualization and analysis approaches presented here can be extended to other areas. These approaches will also be improved by more frequent collection of moderate resolution imagery, as planned by the Landsat and Sentinel-2 programs. In the modern era of global environmental change, use of the Landsat spectral-temporal domain presents new and exciting opportunities for the long-term large-scale study of ecosystem extent, composition, condition, and change.

Use of microcirculatory parameters to evaluate clinical treatments of chronic venous disorder (CVD).

PubMed

Lascasas-Porto, Carmen Lucia; Milhomens, Ana Letícia M; Virgini-Magalhães, Carlos Eduardo; Fernandes, Fabiano F A; Sicuro, Fernando L; Bouskela, Eliete

2008-05-01

To evaluate changes on cutaneous microangiopathy in chronic venous disorder (CVD) after use of Cirkan [venotonic drug containing Ruscus aculeatus (plant extract), hesperidine methylchalcone (flavonoid) and vitamin C], elastic compression stockings (ECS) or no treatment for four weeks. Fifty-five female patients (85 legs), 25 to 57 years, with at least one limb classified as C2,s or C2,3,s (CEAP classification), were allocated consecutively, according to entrance order, in these three groups. Ten healthy women age-matched were also investigated. Using orthogonal polarization spectral technique (noninvasive method), measurements of functional capillary density (FCD, number of capillaries with flowing red blood cells/mm(2)), capillary morphology (CM, % of abnormal capillaries/mm(2)) and diameters (mum) of dermal papilla (DDP), capillary bulk (DCB) and capillary limb (CD) were obtained on the medial perimalleolar region and later analyzed using CapImage software. CVD patients showed significant changes on CD and CM compared to healthy subjects in agreement with our previous findings (J Vasc Surg 43:1037-1044, 2006). On Cirkan-treated patients, after 4 weeks, CD decreased on both limbs and CM improved on the left one, suggesting an amelioration of the chronic venous hypertension. No significant changes could be detected on other patient groups. These results confirm the existence of microcirculatory dysfunction in early stages of CVD, probably due to post-capillary hypertension, and further support the venotonic action of Cirkan.

Mapping burn severity, pine beetle infestation, and their interaction at the High Park Fire

NASA Astrophysics Data System (ADS)

Stone, Brandon

North America's western forests are experiencing wildfire and mountain pine beetle (MPB) disturbances that are unprecedented in the historic record, but it remains unclear whether and how MPB infestation influences post-infestation fire behavior. The 2012 High Park Fire burned in an area that's estimated to have begun a MPB outbreak cycle within five years before the wildfire, resulting in a landscape in which disturbance interactions can be studied. A first step in studying these interactions is mapping regions of beetle infestation and post-fire disturbance. We implemented an approach for mapping beetle infestation and burn severity using as source data three 5 m resolution RapidEye satellite images (two pre-fire, one post-fire). A two-tiered methodology was developed to overcome the spatial limitations of many classification approaches through explicit analyses at both pixel and plot level. Major land cover classes were photo-interpreted at the plot-level and their spectral signature used to classify 5 m images. A new image was generated at 25 m resolution by tabulating the fraction of coincident 5 m pixels in each cover class. The original photo interpretation was then used to train a second classification using as its source image the new 25 m image. Maps were validated using k-fold analysis of the original photo interpretation, field data collected immediately post-fire, and publicly available classifications. To investigate the influence of pre-fire beetle infestation on burn severity within the High Park Fire, we fit a log-linear model of conditional independence to our thematic maps after controlling for forest cover class and slope aspect. Our analysis revealed a high co-occurrence of severe burning and beetle infestation within high elevation lodgepole pine stands, but did not find statistically significant evidence that infected stands were more likely to burn severely than similar uninfected stands. Through an inspection of the year-to-year changes in the class fraction signatures of pixels classified as MPB infestation, we were able to observe increases in infection extent and intensity in the year before the fire. The resulting maps will help to increase our understanding of the process that contributed to the High Park Fire, and we believe that the novel classification approach will allow for improved characterization of forest disturbances.

Change in land use in the Phoenix (1:250,000) Quadrangle, Arizona between 1970 and 1972: Successful use of proposed land use classification system

NASA Technical Reports Server (NTRS)

Place, J. L.

1973-01-01

Changes in land use in the Phoenix (1:250,000 scale) Quadrangle in Arizona have been mapped using only the images from ERTS-1, tending to verify the utility of a land use classification system proposed for use with ERTS images. The period of change investigated was from November 1970 to late summer or early fall, 1972. Seasonal changes also were studied using successive ERTS images. Types of equipment used to aid interpretation included a color additive viewer, a twenty-power magnifier, a density slicer, and a diazo copy machine for making ERTS color composites in hard copy. Types of changes detected have been: (1) cropland or rangeland developed for new residential areas; (2) rangeland converted to new cropland; and (3) possibly new areas of industrial or commercial development. A map of land use previously compiled from air photos was updated in this manner.

48 CFR 22.404-10 - Posting wage determinations and notice.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 48 Federal Acquisition Regulations System 1 2010-10-01 2010-10-01 false Posting wage... Contracts Involving Construction 22.404-10 Posting wage determinations and notice. The contractor must keep a copy of the applicable wage determination (and any approved additional classifications) posted at...

Proteomic analysis of ethanol-induced embryotoxicity in cultured post-implantation rat embryos.

PubMed

Usami, Makoto; Mitsunaga, Katsuyoshi; Irie, Tomohiko; Miyajima, Atsuko; Doi, Osamu

2014-04-01

Protein expression changes were examined in day 10.5 rat embryos cultured for 24 hr in the presence of ethanol by using two-dimensional electrophoresis and mass spectrometry. Exposure to ethanol resulted in quantitative changes in many embryonic protein spots (16 decreased and 28 increased) at in vitro embryotoxic concentrations (130 and 195 mM); most changes occurred in a concentration-dependent manner. For these protein spots, 17 proteins were identified, including protein disulfide isomerase A3, alpha-fetoprotein, phosphorylated cofilin-1, and serum albumin. From the gene ontology classification and pathway mapping of the identified proteins, it was found that ethanol affected several biological processes involving oxidative stress and retinoid metabolism.

Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems.

PubMed

Oh, Sang-Il; Kang, Hang-Bong

2017-01-22

To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226 × 370 image, whereas the original selective search method extracted approximately 10 6 × n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset.

Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems

PubMed Central

Oh, Sang-Il; Kang, Hang-Bong

2017-01-01

To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226×370 image, whereas the original selective search method extracted approximately 106×n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset. PMID:28117742

Impact of Flooding on Land Use/ Land Cover Transformation in Wular Lake and its Environs, Kashmir Valley, India Using Geoinformatics

NASA Astrophysics Data System (ADS)

Ahmad, T.; Pandey, A. C.; Kumar, A.

2017-11-01

Wular lake, located at an elevation of 1520 m above sea level in Kashmir valley, India. In the present study, the immediate and long term impact of flood (2014) over the Wular lake environs was analyzed by using satellite images and employing supervised classification technique in GIS environment. The LULC classification was performed on the images of 25th August 2014 (pre flood) and 13th September 2015 (post flood) and was compared, which indicated marked decrease in terrestrial vegetation (23.7 %), agriculture (43.7 %) and water bodies (39.9 %). Overlaying analysis was performed with pre and post flood classified images with reference to the satellite image of 10th September 2014(during flood) which indicated total area inundated during flood was 88.77 km2. With the pre-flood situation, the aquatic vegetation of 34.06 km2, 13.89 km2 of agriculture land and terrestrial vegetation of 3.13 km2 was inundated. In the post flood situation, it was also came into focus that more than the half of the area under water bodies was converted into sand deposits (22.76 km2) due to anomalous increase in siltation. The overlay analysis on post flood classified image indicated that aquatic vegetation followed by agriculture and sand deposits lie within the flood inundated area. Further spatial analysis was performed within the flood inundated area (88.77 km2) with pre and post classified image to understand the situation before and after the flood and to calculate the changes. These land use-land cover transformations signifies the ill effect of flooding on the biodiversity of Wular Lake.

Chelation therapy to treat atherosclerosis, particularly in diabetes: is it time to reconsider?

PubMed

Lamas, Gervasio A; Ergui, Ian

2016-08-01

Case reports and case series have suggested a possible beneficial effect of chelation therapy in patients with atherosclerotic disease. Small randomized trials conducted in patients with angina or peripheral artery disease, however, were not sufficiently powered to provide conclusive evidence on clinical outcomes. The Trial to Assess Chelation Therapy (TACT) was the first randomized trial adequately powered to detect the effects of chelation therapy on clinical endpoints. We discuss results and future research. Expert commentary: Chelation reduced adverse cardiovascular events in a post myocardial infarction (MI) population. Patients with diabetes demonstrated even greater benefit, with a number needed to treat of 6.5 patients to prevent a cardiac event over 5 years, with a 41% relative reduction in risk of a cardiac event (p = 0.0002). These results led to the revision of the ACC/AHA guideline recommendations for chelation therapy, changing its classification from class III to class IIb. TACT2, a replicative trial, will assess the effects of chelation therapy on cardiovascular outcomes in diabetic patients with a prior myocardial infarction. We are seeking participating sites for TACT2.

a Landsat Time-Series Stacks Model for Detection of Cropland Change

NASA Astrophysics Data System (ADS)

Chen, J.; Chen, J.; Zhang, J.

2017-09-01

Global, timely, accurate and cost-effective cropland monitoring with a fine spatial resolution will dramatically improve our understanding of the effects of agriculture on greenhouse gases emissions, food safety, and human health. Time-series remote sensing imagery have been shown particularly potential to describe land cover dynamics. The traditional change detection techniques are often not capable of detecting land cover changes within time series that are severely influenced by seasonal difference, which are more likely to generate pseuso changes. Here,we introduced and tested LTSM ( Landsat time-series stacks model), an improved Continuous Change Detection and Classification (CCDC) proposed previously approach to extract spectral trajectories of land surface change using a dense Landsat time-series stacks (LTS). The method is expected to eliminate pseudo changes caused by phenology driven by seasonal patterns. The main idea of the method is that using all available Landsat 8 images within a year, LTSM consisting of two term harmonic function are estimated iteratively for each pixel in each spectral band .LTSM can defines change area by differencing the predicted and observed Landsat images. The LTSM approach was compared with change vector analysis (CVA) method. The results indicated that the LTSM method correctly detected the "true change" without overestimating the "false" one, while CVA pointed out "true change" pixels with a large number of "false changes". The detection of change areas achieved an overall accuracy of 92.37 %, with a kappa coefficient of 0.676.

Detecting spam comments on Indonesia’s Instagram posts

NASA Astrophysics Data System (ADS)

Septiandri, Ali Akbar; Wibisono, Okiriza

2017-01-01

In this paper we experimented with several feature sets for detecting spam comments in social media contents authored by Indonesian public figures. We define spam comments as comments which have promotional purposes (e.g. referring other users to products and services) and thus not related to the content to which the comments are posted. Three sets of features are evaluated for detecting spams: (1) hand-engineered features such as comment length, number of capital letters, and number of emojis, (2) keyword features such as whether the comment contains advertising words or product-related words, and (3) text features, namely, bag-of-words, TF-IDF, and fastText embeddings, each combined with latent semantic analysis. With 24,000 manually-annotated comments scraped from Instagram posts authored by more than 100 Indonesian public figures, we compared the performance of these feature sets and their combinations using 3 popular classification algorithms: Na¨ıve Bayes, SVM, and XGBoost. We find that using all three feature sets (with fastText embedding for the text features) gave the best F 1-score of 0.9601 on a holdout dataset. More interestingly, fastText embedding combined with hand-engineered features (i.e. without keyword features) yield similar F 1-score of 0.9523, and McNemar’s test failed to reject the hypothesis that the two results are not significantly different. This result is important as keyword features are largely dependent on the dataset and may not be as generalisable as the other feature sets when applied to new data. For future work, we hope to collect bigger and more diverse dataset of Indonesian spam comments, improve our model’s performance and generalisability, and publish a programming package for others to reliably detect spam comments.

Land cover change detection of Hatiya Island, Bangladesh, using remote sensing techniques

NASA Astrophysics Data System (ADS)

Kumar, Lalit; Ghosh, Manoj Kumer

2012-01-01

Land cover change is a significant issue for environmental managers for sustainable management. Remote sensing techniques have been shown to have a high probability of recognizing land cover patterns and change detection due to periodic coverage, data integrity, and provision of data in a broad range of the electromagnetic spectrum. We evaluate the applicability of remote sensing techniques for land cover pattern recognition, as well as land cover change detection of the Hatiya Island, Bangladesh, and quantify land cover changes from 1977 to 1999. A supervised classification approach was used to classify Landsat Enhanced Thematic Mapper (ETM), Thematic Mapper (TM), and Multispectral Scanner (MSS) images into eight major land cover categories. We detected major land cover changes over the 22-year study period. During this period, marshy land, mud, mud with small grass, and bare soil had decreased by 85%, 46%, 44%, and 24%, respectively, while agricultural land, medium forest, forest, and settlement had positive changes of 26%, 45%, 363%, and 59%, respectively. The primary drivers of such landscape change were erosion and accretion processes, human pressure, and the reforestation and land reclamation programs of the Bangladesh Government.

a Two-Step Classification Approach to Distinguishing Similar Objects in Mobile LIDAR Point Clouds

NASA Astrophysics Data System (ADS)

He, H.; Khoshelham, K.; Fraser, C.

2017-09-01

Nowadays, lidar is widely used in cultural heritage documentation, urban modeling, and driverless car technology for its fast and accurate 3D scanning ability. However, full exploitation of the potential of point cloud data for efficient and automatic object recognition remains elusive. Recently, feature-based methods have become very popular in object recognition on account of their good performance in capturing object details. Compared with global features describing the whole shape of the object, local features recording the fractional details are more discriminative and are applicable for object classes with considerable similarity. In this paper, we propose a two-step classification approach based on point feature histograms and the bag-of-features method for automatic recognition of similar objects in mobile lidar point clouds. Lamp post, street light and traffic sign are grouped as one category in the first-step classification for their inter similarity compared with tree and vehicle. A finer classification of the lamp post, street light and traffic sign based on the result of the first-step classification is implemented in the second step. The proposed two-step classification approach is shown to yield a considerable improvement over the conventional one-step classification approach.

Application of Geologic Mapping Techniques and Autonomous Feature Detection to Future Exploration of Europa

NASA Astrophysics Data System (ADS)

Bunte, M. K.; Tanaka, K. L.; Doggett, T.; Figueredo, P. H.; Lin, Y.; Greeley, R.; Saripalli, S.; Bell, J. F.

2013-12-01

Europa's extremely young surface age, evidence for extensive resurfacing, and indications of a sub-surface ocean elevate its astrobiological potential for habitable environments and make it a compelling focus for study. Knowledge of the global distribution and timing of Europan geologic units is a key step in understanding the history of the satellite and for identifying areas relevant for exploration. I have produced a 1:15M scale global geologic map of Europa which represents a proportionate distribution of four unit types and associated features: plains, linea, chaos, and crater materials. Mapping techniques differ somewhat from other planetary maps but do provide a method to establish stratigraphic markers and to illustrate the surface history through four periods of formation as a function of framework lineament cross-cutting relationships. Correlations of observed features on Europa with Earth analogs enforce a multi-process theory for formation rather than the typical reliance on the principle of parsimony. Lenticulae and microchaos are genetically similar and most likely form by diapirism. Platy and blocky chaos units, endmembers of archetypical chaos, are best explained by brine mobilization. Ridges account for the majority of lineaments and may form by a number of methods indicative of local conditions; most form by either tidal pumping or shear heating. The variety of morphologies exhibited by bands indicates that multiple formation mechanisms apply once fracturing of the brittle surface over a ductile subsurface is initiated. Mapping results support the interpretation that Europa's shell has thickened over time resulting in changes in the style and intensity of deformation. Mapping serves as an index for change detection and classification, aids in pre-encounter targeting, and supports the selection of potential landing sites. Highest priority target areas are those which indicate geophysical activity by the presence of volcanic plumes, outgassing, or disrupted surface morphologies. Areas of high interest include lineaments and chaos margins. The limitations on detecting activity at these locations are approximated by studying similar observed conditions on other bodies. By adapting machine learning and data mining techniques to signatures of plumes and morphology, I have demonstrated autonomous rule-based detection of known features using edge-detection and supervised classification methods. These methods successfully detect ≤94% of known volcanic plumes or jets at Io, Enceladus, and comets. They also allow recognition of multiple feature types. Applying these results to conditions expected for Europa enables a prediction of the potential for detection of similar features and enables recommendations for mission concepts to increase the science return and efficiency of future missions to observe Europa. This post-Galileo view of Europa provides a synthesis of the overall history of this unique icy satellite and will be a useful frame of reference for future exploration of the jovian system and other potentially active outer solar system bodies.

Trends in oral drug bioavailability following bariatric surgery: examining the variable extent of impact on exposure of different drug classes.

PubMed

Darwich, Adam S; Henderson, Kathryn; Burgin, Angela; Ward, Nicola; Whittam, Janet; Ammori, Basil J; Ashcroft, Darren M; Rostami-Hodjegan, Amin

2012-11-01

Changes to oral drug bioavailability have been observed post bariatric surgery. However, the magnitude and the direction of changes have not been assessed systematically to provide insights into the parameters governing the observed trends. Understanding these can help with dose adjustments. Analysis of drug characteristics based on a biopharmaceutical classification system is not adequate to explain observed trends in altered oral drug bioavailability following bariatric surgery, although the findings suggest solubility to play an important role. To identify the most commonly prescribed drugs in a bariatric surgery population and to assess existing evidence regarding trends in oral drug bioavailability post bariatric surgery. A retrospective audit was undertaken to document commonly prescribed drugs amongst patients undergoing bariatric surgery in an NHS hospital in the UK and to assess practice for drug administration following bariatric surgery. The available literature was examined for trends relating to drug permeability and solubility with regards to the Biopharmaceutics Classification System (BCS) and main route of elimination. No significant difference in the 'post/pre surgery oral drug exposure ratio' (ppR) was apparent between BCS class I to IV drugs, with regards to dose number (Do) or main route of elimination. Drugs classified as 'solubility limited' displayed an overall reduction as compared with 'freely soluble' compounds, as well as an unaltered and increased ppR. Clinical studies establishing guidelines for commonly prescribed drugs, and the monitoring of drugs exhibiting a narrow therapeutic window or without a readily assessed clinical endpoint, are warranted. Using mechanistically based pharmacokinetic modelling for simulating the multivariate nature of changes in drug exposure may serve as a useful tool in the further understanding of postoperative trends in oral drug exposure and in developing practical clinical guidance. © 2012 The Authors. British Journal of Clinical Pharmacology © 2012 The British Pharmacological Society.

Unsupervised active learning based on hierarchical graph-theoretic clustering.

PubMed

Hu, Weiming; Hu, Wei; Xie, Nianhua; Maybank, Steve

2009-10-01

Most existing active learning approaches are supervised. Supervised active learning has the following problems: inefficiency in dealing with the semantic gap between the distribution of samples in the feature space and their labels, lack of ability in selecting new samples that belong to new categories that have not yet appeared in the training samples, and lack of adaptability to changes in the semantic interpretation of sample categories. To tackle these problems, we propose an unsupervised active learning framework based on hierarchical graph-theoretic clustering. In the framework, two promising graph-theoretic clustering algorithms, namely, dominant-set clustering and spectral clustering, are combined in a hierarchical fashion. Our framework has some advantages, such as ease of implementation, flexibility in architecture, and adaptability to changes in the labeling. Evaluations on data sets for network intrusion detection, image classification, and video classification have demonstrated that our active learning framework can effectively reduce the workload of manual classification while maintaining a high accuracy of automatic classification. It is shown that, overall, our framework outperforms the support-vector-machine-based supervised active learning, particularly in terms of dealing much more efficiently with new samples whose categories have not yet appeared in the training samples.

Unified framework for triaxial accelerometer-based fall event detection and classification using cumulants and hierarchical decision tree classifier.

PubMed

Kambhampati, Satya Samyukta; Singh, Vishal; Manikandan, M Sabarimalai; Ramkumar, Barathram

2015-08-01

In this Letter, the authors present a unified framework for fall event detection and classification using the cumulants extracted from the acceleration (ACC) signals acquired using a single waist-mounted triaxial accelerometer. The main objective of this Letter is to find suitable representative cumulants and classifiers in effectively detecting and classifying different types of fall and non-fall events. It was discovered that the first level of the proposed hierarchical decision tree algorithm implements fall detection using fifth-order cumulants and support vector machine (SVM) classifier. In the second level, the fall event classification algorithm uses the fifth-order cumulants and SVM. Finally, human activity classification is performed using the second-order cumulants and SVM. The detection and classification results are compared with those of the decision tree, naive Bayes, multilayer perceptron and SVM classifiers with different types of time-domain features including the second-, third-, fourth- and fifth-order cumulants and the signal magnitude vector and signal magnitude area. The experimental results demonstrate that the second- and fifth-order cumulant features and SVM classifier can achieve optimal detection and classification rates of above 95%, as well as the lowest false alarm rate of 1.03%.

Reliability of classification for post-traumatic ankle osteoarthritis.

PubMed

Claessen, Femke M A P; Meijer, Diederik T; van den Bekerom, Michel P J; Gevers Deynoot, Barend D J; Mallee, Wouter H; Doornberg, Job N; van Dijk, C Niek

2016-04-01

The purpose of this study was to identify the most reliable classification system for clinical outcome studies to categorize post-traumatic-fracture-osteoarthritis. A total of 118 orthopaedic surgeons and residents-gathered in the Ankle Platform Study Collaborative Science of Variation Group-evaluated 128 anteroposterior and lateral radiographs of patients after a bi- or trimalleolar ankle fracture on a Web-based platform in order to rate post-traumatic osteoarthritis according to the classification systems coined by (1) van Dijk, (2) Kellgren, and (3) Takakura. Reliability was evaluated with the use of the Siegel and Castellan's multirater kappa measure. Differences between classification systems were compared using the two-sample Z-test. Interobserver agreement of surgeons who participated in the survey was fair for the van Dijk osteoarthritis scale (k = 0.24), and poor for the Takakura (k = 0.19) and the Kellgren systems (k = 0.18) according to the categorical rating of Landis and Koch. This difference in one categorical rating was found to be significant (p < 0.001, CI 0.046-0.053) with the high numbers of observers and cases available. This study documents fair interobserver agreement for the van Dijk osteoarthritis scale, and poor interobserver agreement for the Takakura and Kellgren osteoarthritis classification systems. Because of the low interobserver agreement for the van Dijk, Kellgren, and Takakura classification systems, those systems cannot be used for clinical decision-making. Development of diagnostic criteria on basis of consecutive patients, Level II.

Local functional connectivity density is closely associated with the response of electroconvulsive therapy in major depressive disorder.

PubMed

Wang, Jiaojian; Wei, Qiang; Yuan, Xinru; Jiang, Xiaoyan; Xu, Jinping; Zhou, Xiaoqin; Tian, Yanghua; Wang, Kai

2018-01-01

Electroconvulsive therapy (ECT) has been demonstrated to be an effective treatment of major depressive disorder (MDD). However, the neuroanatomical basis of response to ECT is still largely unknown. In present study, we used functional connectivity density (FCD) and resting-state functional connectivity (RSFC) to identify the relationship between the changes of resting-state activities and ECT responses in 23 MDD patients before and after ECT. In addition, the identified neural indices as classification characteristics were entered into multivariate pattern analysis using linear support vector machine (SVM) to classify 23 MDD patients before ECT from 25 gender, age and years of education matched healthy controls. We found that the changes of local FCD (lFCD), not long-range FCD, of the left pre-/postcentral gyrus (Pre-/postCG), left superior temporal gyrus (STG), and right STG were significantly correlated with the changes of Hamilton Rating Scale for Depression (HRSD) scores in MDD patients before and after ECT. The subsequent functional connectivity analysis revealed significantly decreased functional connectivity between right STG and right intraparietal sulcus (IPS) in MDD after ECT in spite of no correlation with HRSD scores. Finally, SVM-based classification achieved an accuracy of 72.92% with a sensitivity of 73.91% and a specificity of 72% by leave-one-out cross-validation. Our findings indicated that Pre-/postCG and bilateral STG play an important role in response of ECT in MDD patients, and the lFCD in these areas may serve as a biomarker for predicting ECT response. Copyright © 2017 Elsevier B.V. All rights reserved.

Recent forest cover changes (2002-2015) in the Southern Carpathians: A case study of the Iezer Mountains, Romania.

PubMed

Mihai, Bogdan; Săvulescu, Ionuț; Rujoiu-Mare, Marina; Nistor, Constantin

2017-12-01

The paper explores the dynamics of the forest cover change in the Iezer Mountains, part of Southern Carpathians, in the context of the forest ownership recovery and deforestation processes, combined with the effects of biotic and abiotic disturbances. The aim of the study is to map and evaluate the typology and the spatial extension of changes in the montane forest cover between 700 and 2462m a.s.l., sampling all the representative Carpathian ecosystems, from the European beech zone up to the spruce-fir zone and the subalpine-alpine pastures. The methodology uses a change detection analysis of satellite imagery with Landsat ETM+/OLI and Sentinel-2 MSI data. The workflow started with a complete calibration of multispectral data from 2002, before the massive forest restitution to private owners, after the Law 247/2005 empowerment, and 2015, the intensification of deforestation process. For the data classification, a Maximum Likelihood supervised classification algorithm was utilized. The forest change map was developed after combining the classifications in a unitary formula using image difference. The principal outcome of the research identifies the type of forest cover change using a quantitative formula. This information can be integrated in the future decision-making strategies for forest stand management and sustainable development. Copyright © 2017 Elsevier B.V. All rights reserved.

The assessment of post-vasectomy pain in mice using behaviour and the Mouse Grimace Scale.

PubMed

Leach, Matthew C; Klaus, Kristel; Miller, Amy L; Scotto di Perrotolo, Maud; Sotocinal, Susana G; Flecknell, Paul A

2012-01-01

Current behaviour-based pain assessments for laboratory rodents have significant limitations. Assessment of facial expression changes, as a novel means of pain scoring, may overcome some of these limitations. The Mouse Grimace Scale appears to offer a means of assessing post-operative pain in mice that is as effective as manual behavioural-based scoring, without the limitations of such schemes. Effective assessment of post-operative pain is not only critical for animal welfare, but also the validity of science using animal models. This study compared changes in behaviour assessed using both an automated system ("HomeCageScan") and using manual analysis with changes in facial expressions assessed using the Mouse Grimace Scale (MGS). Mice (n = 6/group) were assessed before and after surgery (scrotal approach vasectomy) and either received saline, meloxicam or bupivacaine. Both the MGS and manual scoring of pain behaviours identified clear differences between the pre and post surgery periods and between those animals receiving analgesia (20 mg/kg meloxicam or 5 mg/kg bupivacaine) or saline post-operatively. Both of these assessments were highly correlated with those showing high MGS scores also exhibiting high frequencies of pain behaviours. Automated behavioural analysis in contrast was only able to detect differences between the pre and post surgery periods. In conclusion, both the Mouse Grimace Scale and manual scoring of pain behaviours are assessing the presence of post-surgical pain, whereas automated behavioural analysis could be detecting surgical stress and/or post-surgical pain. This study suggests that the Mouse Grimace Scale could prove to be a quick and easy means of assessing post-surgical pain, and the efficacy of analgesic treatment in mice that overcomes some of the limitations of behaviour-based assessment schemes.

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

PubMed

Zerrouki, Nabil; Harrou, Fouzi; Sun, Ying; Houacine, Amrane

2016-12-01

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

Detection of Post-Therapeutic Effects in Breast Carcinoma Using Hard X-Ray Index of Refraction Computed Tomography - A Feasibility Study.

PubMed

Grandl, Susanne; Sztrókay-Gaul, Anikó; Mittone, Alberto; Gasilov, Sergey; Brun, Emmanuel; Bravin, Alberto; Mayr, Doris; Auweter, Sigrid D; Hellerhoff, Karin; Reiser, Maximilian; Coan, Paola

2016-01-01

Neoadjuvant chemotherapy is the state-of-the-art treatment in advanced breast cancer. A correct visualization of the post-therapeutic tumor size is of high prognostic relevance. X-ray phase-contrast computed tomography (PC-CT) has been shown to provide improved soft-tissue contrast at a resolution formerly restricted to histopathology, at low doses. This study aimed at assessing ex-vivo the potential use of PC-CT for visualizing the effects of neoadjuvant chemotherapy on breast carcinoma. The analysis was performed on two ex-vivo formalin-fixed mastectomy samples containing an invasive carcinoma removed from two patients treated with neoadjuvant chemotherapy. Images were matched with corresponding histological slices. The visibility of typical post-therapeutic tissue changes was assessed and compared to results obtained with conventional clinical imaging modalities. PC-CT depicted the different tissue types with an excellent correlation to histopathology. Post-therapeutic tissue changes were correctly visualized and the residual tumor mass could be detected. PC-CT outperformed clinical imaging modalities in the detection of chemotherapy-induced tissue alterations including post-therapeutic tumor size. PC-CT might become a unique diagnostic tool in the prediction of tumor response to neoadjuvant chemotherapy. PC-CT might be used to assist during histopathological diagnosis, offering a high-resolution and high-contrast virtual histological tool for the accurate delineation of tumor boundaries.

A sonographic approach to prenatal classification of congenital spine anomalies

PubMed Central

Robertson, Meiri; Sia, Sock Bee

2015-01-01

Abstract Objective: To develop a classification system for congenital spine anomalies detected by prenatal ultrasound. Methods: Data were collected from fetuses with spine abnormalities diagnosed in our institution over a five‐year period between June 2005 and June 2010. The ultrasound images were analysed to determine which features were associated with different congenital spine anomalies. Findings of the prenatal ultrasound images were correlated with other prenatal imaging, post mortem findings, post mortem imaging, neonatal imaging, karyotype, and other genetic workup. Data from published case reports of prenatal diagnosis of rare congenital spine anomalies were analysed to provide a comprehensive work. Results: During the study period, eighteen cases of spine abnormalities were diagnosed in 7819 women. The mean gestational age at diagnosis was 18.8w ± 2.2 SD. While most cases represented open NTD, a spectrum of vertebral abnormalities were diagnosed prenatally. These included hemivertebrae, block vertebrae, cleft or butterfly vertebrae, sacral agenesis, and a lipomeningocele. The most sensitive features for diagnosis of a spine abnormality included flaring of the vertebral arch ossification centres, abnormal spine curvature, and short spine length. While reported findings at the time of diagnosis were often conservative, retrospective analysis revealed good correlation with radiographic imaging. 3D imaging was found to be a valuable tool in many settings. Conclusions: Analysis of the study findings showed prenatal ultrasound allowed detection of disruption to the normal appearances of the fetal spine. Using the three features of flaring of the vertebral arch ossification centres, abnormal spine curvature, and short spine length, an algorithm was devised to aid with the diagnosis of spine anomalies for those who perform and report prenatal ultrasound. PMID:28191204

Accurate Detection of Dysmorphic Nuclei Using Dynamic Programming and Supervised Classification.

PubMed

Verschuuren, Marlies; De Vylder, Jonas; Catrysse, Hannes; Robijns, Joke; Philips, Wilfried; De Vos, Winnok H

2017-01-01

A vast array of pathologies is typified by the presence of nuclei with an abnormal morphology. Dysmorphic nuclear phenotypes feature dramatic size changes or foldings, but also entail much subtler deviations such as nuclear protrusions called blebs. Due to their unpredictable size, shape and intensity, dysmorphic nuclei are often not accurately detected in standard image analysis routines. To enable accurate detection of dysmorphic nuclei in confocal and widefield fluorescence microscopy images, we have developed an automated segmentation algorithm, called Blebbed Nuclei Detector (BleND), which relies on two-pass thresholding for initial nuclear contour detection, and an optimal path finding algorithm, based on dynamic programming, for refining these contours. Using a robust error metric, we show that our method matches manual segmentation in terms of precision and outperforms state-of-the-art nuclear segmentation methods. Its high performance allowed for building and integrating a robust classifier that recognizes dysmorphic nuclei with an accuracy above 95%. The combined segmentation-classification routine is bound to facilitate nucleus-based diagnostics and enable real-time recognition of dysmorphic nuclei in intelligent microscopy workflows.

Accurate Detection of Dysmorphic Nuclei Using Dynamic Programming and Supervised Classification

PubMed Central

Verschuuren, Marlies; De Vylder, Jonas; Catrysse, Hannes; Robijns, Joke; Philips, Wilfried

2017-01-01

A vast array of pathologies is typified by the presence of nuclei with an abnormal morphology. Dysmorphic nuclear phenotypes feature dramatic size changes or foldings, but also entail much subtler deviations such as nuclear protrusions called blebs. Due to their unpredictable size, shape and intensity, dysmorphic nuclei are often not accurately detected in standard image analysis routines. To enable accurate detection of dysmorphic nuclei in confocal and widefield fluorescence microscopy images, we have developed an automated segmentation algorithm, called Blebbed Nuclei Detector (BleND), which relies on two-pass thresholding for initial nuclear contour detection, and an optimal path finding algorithm, based on dynamic programming, for refining these contours. Using a robust error metric, we show that our method matches manual segmentation in terms of precision and outperforms state-of-the-art nuclear segmentation methods. Its high performance allowed for building and integrating a robust classifier that recognizes dysmorphic nuclei with an accuracy above 95%. The combined segmentation-classification routine is bound to facilitate nucleus-based diagnostics and enable real-time recognition of dysmorphic nuclei in intelligent microscopy workflows. PMID:28125723

Automatic detection of malaria parasite in blood images using two parameters.

PubMed

Kim, Jong-Dae; Nam, Kyeong-Min; Park, Chan-Young; Kim, Yu-Seop; Song, Hye-Jeong

2015-01-01

Malaria must be diagnosed quickly and accurately at the initial infection stage and treated early to cure it properly. The malaria diagnosis method using a microscope requires much labor and time of a skilled expert and the diagnosis results vary greatly between individual diagnosticians. Therefore, to be able to measure the malaria parasite infection quickly and accurately, studies have been conducted for automated classification techniques using various parameters. In this study, by measuring classification technique performance according to changes of two parameters, the parameter values were determined that best distinguish normal from plasmodium-infected red blood cells. To reduce the stain deviation of the acquired images, a principal component analysis (PCA) grayscale conversion method was used, and as parameters, we used a malaria infected area and a threshold value used in binarization. The parameter values with the best classification performance were determined by selecting the value (72) corresponding to the lowest error rate on the basis of cell threshold value 128 for the malaria threshold value for detecting plasmodium-infected red blood cells.

Oral cancer screening: serum Raman spectroscopic approach

NASA Astrophysics Data System (ADS)

Sahu, Aditi K.; Dhoot, Suyash; Singh, Amandeep; Sawant, Sharada S.; Nandakumar, Nikhila; Talathi-Desai, Sneha; Garud, Mandavi; Pagare, Sandeep; Srivastava, Sanjeeva; Nair, Sudhir; Chaturvedi, Pankaj; Murali Krishna, C.

2015-11-01

Serum Raman spectroscopy (RS) has previously shown potential in oral cancer diagnosis and recurrence prediction. To evaluate the potential of serum RS in oral cancer screening, premalignant and cancer-specific detection was explored in the present study using 328 subjects belonging to healthy controls, premalignant, disease controls, and oral cancer groups. Spectra were acquired using a Raman microprobe. Spectral findings suggest changes in amino acids, lipids, protein, DNA, and β-carotene across the groups. A patient-wise approach was employed for data analysis using principal component linear discriminant analysis. In the first step, the classification among premalignant, disease control (nonoral cancer), oral cancer, and normal samples was evaluated in binary classification models. Thereafter, two screening-friendly classification approaches were explored to further evaluate the clinical utility of serum RS: a single four-group model and normal versus abnormal followed by determining the type of abnormality model. Results demonstrate the feasibility of premalignant and specific cancer detection. The normal versus abnormal model yields better sensitivity and specificity rates of 64 and 80% these rates are comparable to standard screening approaches. Prospectively, as the current screening procedure of visual inspection is useful mainly for high-risk populations, serum RS may serve as a useful adjunct for early and specific detection of oral precancers and cancer.

Multivariate Classification of Structural MRI Data Detects Chronic Low Back Pain

PubMed Central

Ung, Hoameng; Brown, Justin E.; Johnson, Kevin A.; Younger, Jarred; Hush, Julia; Mackey, Sean

2014-01-01

Chronic low back pain (cLBP) has a tremendous personal and socioeconomic impact, yet the underlying pathology remains a mystery in the majority of cases. An objective measure of this condition, that augments self-report of pain, could have profound implications for diagnostic characterization and therapeutic development. Contemporary research indicates that cLBP is associated with abnormal brain structure and function. Multivariate analyses have shown potential to detect a number of neurological diseases based on structural neuroimaging. Therefore, we aimed to empirically evaluate such an approach in the detection of cLBP, with a goal to also explore the relevant neuroanatomy. We extracted brain gray matter (GM) density from magnetic resonance imaging scans of 47 patients with cLBP and 47 healthy controls. cLBP was classified with an accuracy of 76% by support vector machine analysis. Primary drivers of the classification included areas of the somatosensory, motor, and prefrontal cortices—all areas implicated in the pain experience. Differences in areas of the temporal lobe, including bordering the amygdala, medial orbital gyrus, cerebellum, and visual cortex, were also useful for the classification. Our findings suggest that cLBP is characterized by a pattern of GM changes that can have discriminative power and reflect relevant pathological brain morphology. PMID:23246778

The preattentive processing of major vs. minor chords in the human brain: An event-related potential study.

PubMed

Virtala, Paula; Berg, Venla; Kivioja, Maari; Purhonen, Juha; Salmenkivi, Marko; Paavilainen, Petri; Tervaniemi, Mari

2011-01-10

Western music has two classifications that are highly familiar to all Western listeners: the dichotomy between the major and minor modalities and consonance vs. dissonance. We aimed at determining whether these classifications already take place at the level of the elicitation of the change-related mismatch negativity (MMN) component of the event-related potential (ERP). To this end, we constructed an oddball-paradigm with root minor, dissonant and inverted major chords in a context of root major chords. These stimuli were composed so that the standard and deviant chords did not include a physically deviant frequency which could cause the MMN. The standard chords were transposed into 12 different keys (=pitch levels) and delivered to the participants while they were watching a silent movie (ignore condition) or detecting softer target sounds (detection condition). In the ignore condition, the MMN was significant for all but inverted major chords. In the detection condition, the MMN was significant for dissonant chords and soft target chords. Our results indicate that the processes underlying MMN are able to make discriminations which are qualitative by nature. Whether the classifications between major and minor modalities and consonance vs. dissonance are innate or based on implicit learning remains a question for the future. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Comparing experts and novices in Martian surface feature change detection and identification

NASA Astrophysics Data System (ADS)

Wardlaw, Jessica; Sprinks, James; Houghton, Robert; Muller, Jan-Peter; Sidiropoulos, Panagiotis; Bamford, Steven; Marsh, Stuart

2018-02-01

Change detection in satellite images is a key concern of the Earth Observation field for environmental and climate change monitoring. Satellite images also provide important clues to both the past and present surface conditions of other planets, which cannot be validated on the ground. With the volume of satellite imagery continuing to grow, the inadequacy of computerised solutions to manage and process imagery to the required professional standard is of critical concern. Whilst studies find the crowd sourcing approach suitable for the counting of impact craters in single images, images of higher resolution contain a much wider range of features, and the performance of novices in identifying more complex features and detecting change, remains unknown. This paper presents a first step towards understanding whether novices can identify and annotate changes in different geomorphological features. A website was developed to enable visitors to flick between two images of the same location on Mars taken at different times and classify 1) if a surface feature changed and if so, 2) what feature had changed from a pre-defined list of six. Planetary scientists provided ;expert; data against which classifications made by novices could be compared when the project subsequently went public. Whilst no significant difference was found in images identified with surface changes by expert and novices, results exhibited differences in consensus within and between experts and novices when asked to classify the type of change. Experts demonstrated higher levels of agreement in classification of changes as dust devil tracks, slope streaks and impact craters than other features, whilst the consensus of novices was consistent across feature types; furthermore, the level of consensus amongst regardless of feature type. These trends are secondary to the low levels of consensus found, regardless of feature type or classifier expertise. These findings demand the attention of researchers who want to use crowd-sourcing for similar scientific purposes, particularly for the supervised training of computer algorithms, and inform the scope and design of future projects.

[A research on real-time ventricular QRS classification methods for single-chip-microcomputers].

PubMed

Peng, L; Yang, Z; Li, L; Chen, H; Chen, E; Lin, J

1997-05-01

Ventricular QRS classification is key technique of ventricular arrhythmias detection in single-chip-microcomputer based dynamic electrocardiogram real-time analyser. This paper adopts morphological feature vector including QRS amplitude, interval information to reveal QRS morphology. After studying the distribution of QRS morphology feature vector of MIT/BIH DB ventricular arrhythmia files, we use morphological feature vector cluster to classify multi-morphology QRS. Based on the method, morphological feature parameters changing method which is suitable to catch occasional ventricular arrhythmias is presented. Clinical experiments verify missed ventricular arrhythmia is less than 1% by this method.

Spectroscopic Detection of Caries Lesions

PubMed Central

Ruohonen, Mika; Palo, Katri; Alander, Jarmo

2013-01-01

Background. A caries lesion causes changes in the optical properties of the affected tissue. Currently a caries lesion can be detected only at a relatively late stage of development. Caries diagnosis also suffers from high interobserver variance. Methods. This is a pilot study to test the suitability of an optical diffuse reflectance spectroscopy for caries diagnosis. Reflectance visible/near-infrared spectroscopy (VIS/NIRS) was used to measure caries lesions and healthy enamel on extracted human teeth. The results were analysed with a computational algorithm in order to find a rule-based classification method to detect caries lesions. Results. The classification indicated that the measured points of enamel could be assigned to one of three classes: healthy enamel, a caries lesion, and stained healthy enamel. The features that enabled this were consistent with theory. Conclusions. It seems that spectroscopic measurements can help to reduce false positives at in vitro setting. However, further research is required to evaluate the strength of the evidence for the method's performance. PMID:27006907

Automatic classification of transiently evoked otoacoustic emissions using an artificial neural network.

PubMed

Buller, G; Lutman, M E

1998-08-01

The increasing use of transiently evoked otoacoustic emissions (TEOAE) in large neonatal hearing screening programmes makes a standardized method of response classification desirable. Until now methods have been either subjective or based on arbitrary response characteristics. This study takes an expert system approach to standardize the subjective judgements of an experienced scorer. The method that is developed comprises three stages. First, it transforms TEOAEs from waveforms in the time domain into a simplified parameter set. Second, the parameter set is classified by an artificial neural network that has been taught on a large database TEOAE waveforms and corresponding expert scores. Third, additional fuzzy logic rules automatically detect probable artefacts in the waveforms and synchronized spontaneous emission components. In this way, the knowledge of the experienced scorer is encapsulated in the expert system software and thereafter can be accessed by non-experts. Teaching and evaluation of the neural network was based on TEOAEs from a database totalling 2190 neonatal hearing screening tests. The database was divided into learning and test groups with 820 and 1370 waveforms respectively. From each recorded waveform a set of 12 parameters was calculated, representing signal static and dynamic properties. The artifical network was taught with parameter sets of only the learning groups. Reproduction of the human scorer classification by the neural net in the learning group showed a sensitivity for detecting screen fails of 99.3% (299 from 301 failed results on subjective scoring) and a specificity for detecting screen passes of 81.1% (421 of 519 pass results). To quantify the post hoc performance of the net (generalization), the test group was then presented to the network input. Sensitivity was 99.4% (474 from 477) and specificity was 87.3% (780 from 893). To check the efficiency of the classification method, a second learning group was selected out of the previous test group, and the previous learning group was used as the test group. Repeating learning and test procedures yielded 99.3% sensitivity and 80.7% specificity for reproduction, and 99.4% sensitivity and 86.7% specificity for generalization. In all respects, performance was better than for a previously optimized method based simply on cross-correlation between replicate non-linear waveforms. It is concluded that classification methods based on neural networks show promise for application to large neonatal screening programmes utilizing TEOAEs.

A study to compare traditional and constructivism-based instruction of a high school biology unit on biosystematics

NASA Astrophysics Data System (ADS)

Saigo, Barbara Woodworth

The researcher collaborated with four high school biology teachers who had been involved for 2-1/2 years in a constructivism-based professional development experience that emphasized teaching for conceptual change and using classroom-based inquiry as a basis for making instructional decisions. The researcher and teachers designed a five-day instructional unit on biosystematics using two contrasting approaches, comprising the treatment variable. The "traditional" unit emphasized lecture, written materials, and some laboratory activities. The "constructivist" unit emphasized a specific, inquiry-based, conceptual change strategy and collaborative learning. The study used a quasi-experimental, factorial design to explore impact of instructional approach (the treatment variable) on student performance (the dependent variable) on repeated measures (three) of a biology concept test. Additional independent variables considered were gender, cumulative GPA, and the section in which students were enrolled. Scores on the biology concept test were compiled for the 3 constructivist sections (N = 44) and the 3 traditional sections (N = 42). Analysis of Covariance (ANCOVA) was applied. The main findings in regard to the primary research question were that instructional approach did not have a significant relationship to immediate post test scores or gain, but that one month after instruction students in the constructivist group demonstrated less loss of gain than those in the traditional group; i.e., their longer-term retention was greater. Also, GPA*instructional approach effects were detected for post-post-test gain. GPA and gender were significantly associated with pre-test, post-test, and post-post scores; however, in terms of change (gain) from pre-test to post-test and pre-test to post-post-test, GPA and gender were not significant effects. Section was a significant effect for all three tests, in terms of both score and gain. Gender*section effects were detected for post-test gain and post-post-test scores.

Detecting Mild Traumatic Brain Injury Using Resting State Magnetoencephalographic Connectivity

PubMed Central

da Costa, Leodante; Jetly, Rakesh; Pang, Elizabeth W.; Taylor, Margot J.

2016-01-01

Accurate means to detect mild traumatic brain injury (mTBI) using objective and quantitative measures remain elusive. Conventional imaging typically detects no abnormalities despite post-concussive symptoms. In the present study, we recorded resting state magnetoencephalograms (MEG) from adults with mTBI and controls. Atlas-guided reconstruction of resting state activity was performed for 90 cortical and subcortical regions, and calculation of inter-regional oscillatory phase synchrony at various frequencies was performed. We demonstrate that mTBI is associated with reduced network connectivity in the delta and gamma frequency range (>30 Hz), together with increased connectivity in the slower alpha band (8–12 Hz). A similar temporal pattern was associated with correlations between network connectivity and the length of time between the injury and the MEG scan. Using such resting state MEG network synchrony we were able to detect mTBI with 88% accuracy. Classification confidence was also correlated with clinical symptom severity scores. These results provide the first evidence that imaging of MEG network connectivity, in combination with machine learning, has the potential to accurately detect and determine the severity of mTBI. PMID:27906973

Optical remote sensing for forest area estimation

Treesearch

Randolph H. Wynne; Richard G. Oderwald; Gregory A. Reams; John A. Scrivani

2000-01-01

The air photo dot-count method is now widely and successfully used for estimating operational forest area in the USDA Forest Inventory and Analysis (FIA) program. Possible alternatives that would provide for more frequent updates, spectral change detection, and maps of forest area include the AVHRR calibration center technique and various Landsat TM classification...

Detecting Changes Following the Provision of Assistive Devices: Utility of the WHO-DAS II

ERIC Educational Resources Information Center

Raggi, Alberto

2010-01-01

The World Health Organization Disability Assessment Schedule II (WHO-DAS II) is a non-disease-specific International Classification of Functioning, Disability, and Health-based disability assessment instrument developed to measure activity limitations and restrictions to participation. The aim of this pilot study is to evaluate WHO-DAS II…

A signal-based fault detection and classification method for heavy haul wagons

NASA Astrophysics Data System (ADS)

Li, Chunsheng; Luo, Shihui; Cole, Colin; Spiryagin, Maksym; Sun, Yanquan

2017-12-01

This paper proposes a signal-based fault detection and isolation (FDI) system for heavy haul wagons considering the special requirements of low cost and robustness. The sensor network of the proposed system consists of just two accelerometers mounted on the front left and rear right of the carbody. Seven fault indicators (FIs) are proposed based on the cross-correlation analyses of the sensor-collected acceleration signals. Bolster spring fault conditions are focused on in this paper, including two different levels (small faults and moderate faults) and two locations (faults in the left and right bolster springs of the first bogie). A fully detailed dynamic model of a typical 40t axle load heavy haul wagon is developed to evaluate the deterioration of dynamic behaviour under proposed fault conditions and demonstrate the detectability of the proposed FDI method. Even though the fault conditions considered in this paper did not deteriorate the wagon dynamic behaviour dramatically, the proposed FIs show great sensitivity to the bolster spring faults. The most effective and efficient FIs are chosen for fault detection and classification. Analysis results indicate that it is possible to detect changes in bolster stiffness of ±25% and identify the fault location.

An Unsupervised kNN Method to Systematically Detect Changes in Protein Localization in High-Throughput Microscopy Images.

PubMed

Lu, Alex Xijie; Moses, Alan M

2016-01-01

Despite the importance of characterizing genes that exhibit subcellular localization changes between conditions in proteome-wide imaging experiments, many recent studies still rely upon manual evaluation to assess the results of high-throughput imaging experiments. We describe and demonstrate an unsupervised k-nearest neighbours method for the detection of localization changes. Compared to previous classification-based supervised change detection methods, our method is much simpler and faster, and operates directly on the feature space to overcome limitations in needing to manually curate training sets that may not generalize well between screens. In addition, the output of our method is flexible in its utility, generating both a quantitatively ranked list of localization changes that permit user-defined cut-offs, and a vector for each gene describing feature-wise direction and magnitude of localization changes. We demonstrate that our method is effective at the detection of localization changes using the Δrpd3 perturbation in Saccharomyces cerevisiae, where we capture 71.4% of previously known changes within the top 10% of ranked genes, and find at least four new localization changes within the top 1% of ranked genes. The results of our analysis indicate that simple unsupervised methods may be able to identify localization changes in images without laborious manual image labelling steps.

A signature dissimilarity measure for trabecular bone texture in knee radiographs

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

Woloszynski, T.; Podsiadlo, P.; Stachowiak, G. W.

Purpose: The purpose of this study is to develop a dissimilarity measure for the classification of trabecular bone (TB) texture in knee radiographs. Problems associated with the traditional extraction and selection of texture features and with the invariance to imaging conditions such as image size, anisotropy, noise, blur, exposure, magnification, and projection angle were addressed. Methods: In the method developed, called a signature dissimilarity measure (SDM), a sum of earth mover's distances calculated for roughness and orientation signatures is used to quantify dissimilarities between textures. Scale-space theory was used to ensure scale and rotation invariance. The effects of image size,more » anisotropy, noise, and blur on the SDM developed were studied using computer generated fractal texture images. The invariance of the measure to image exposure, magnification, and projection angle was studied using x-ray images of human tibia head. For the studies, Mann-Whitney tests with significance level of 0.01 were used. A comparison study between the performances of a SDM based classification system and other two systems in the classification of Brodatz textures and the detection of knee osteoarthritis (OA) were conducted. The other systems are based on weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHARM) and local binary patterns (LBP). Results: Results obtained indicate that the SDM developed is invariant to image exposure (2.5-30 mA s), magnification (x1.00-x1.35), noise associated with film graininess and quantum mottle (<25%), blur generated by a sharp film screen, and image size (>64x64 pixels). However, the measure is sensitive to changes in projection angle (>5 deg.), image anisotropy (>30 deg.), and blur generated by a regular film screen. For the classification of Brodatz textures, the SDM based system produced comparable results to the LBP system. For the detection of knee OA, the SDM based system achieved 78.8% classification accuracy and outperformed the WND-CHARM system (64.2%). Conclusions: The SDM is well suited for the classification of TB texture images in knee OA detection and may be useful for the texture classification of medical images in general.« less

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.

JointMMCC: Joint Maximum-Margin Classification and Clustering of Imaging Data

PubMed Central

Filipovych, Roman; Resnick, Susan M.; Davatzikos, Christos

2012-01-01

A number of conditions are characterized by pathologies that form continuous or nearly-continuous spectra spanning from the absence of pathology to very pronounced pathological changes (e.g., normal aging, Mild Cognitive Impairment, Alzheimer's). Moreover, diseases are often highly heterogeneous with a number of diagnostic subcategories or subconditions lying within the spectra (e.g., Autism Spectrum Disorder, schizophrenia). Discovering coherent subpopulations of subjects within the spectrum of pathological changes may further our understanding of diseases, and potentially identify subconditions that require alternative or modified treatment options. In this paper, we propose an approach that aims at identifying coherent subpopulations with respect to the underlying MRI in the scenario where the condition is heterogeneous and pathological changes form a continuous spectrum. We describe a Joint Maximum-Margin Classification and Clustering (JointMMCC) approach that jointly detects the pathologic population via semi-supervised classification, as well as disentangles heterogeneity of the pathological cohort by solving a clustering subproblem. We propose an efficient solution to the non-convex optimization problem associated with JointMMCC. We apply our proposed approach to an MRI study of aging, and identify coherent subpopulations (i.e., clusters) of cognitively less stable adults. PMID:22328179

39 CFR 241.1 - Post offices.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 39 Postal Service 1 2010-07-01 2010-07-01 false Post offices. 241.1 Section 241.1 Postal Service... DISCONTINUANCE § 241.1 Post offices. (a) Establishment. See § 113.1 of this chapter. (b) Classification. As of July 1 each year, post offices are classified by the Postmaster General based on the allowable postal...

Monitoring urban land cover with the use of satellite remote sensing techniques as a means of flood risk assessment in Cyprus

NASA Astrophysics Data System (ADS)

Alexakis, Dimitris; Hadjimitsis, Diofantos; Agapiou, Athos; Themistocleous, Kyriacos; Retalis, Adrianos

2011-11-01

The increase of flood inundation occuring in different regions all over the world have enhanced the need for effective flood risk management. As floods frequency is increasing with a steady rate due to ever increasing human activities on physical floodplains there is a respectively increasing of financial destructive impact of floods. A flood can be determined as a mass of water that produces runoff on land that is not normally covered by water. However, earth observation techniques such as satellite remote sensing can contribute toward a more efficient flood risk mapping according to EU Directives of 2007/60. This study strives to highlight the need of digital mapping of urban sprawl in a catchment area in Cyprus and the assessment of its contribution to flood risk. The Yialias river (Nicosia, Cyprus) was selected as case study where devastating flash floods events took place at 2003 and 2009. In order to search the diachronic land cover regime of the study area multi-temporal satellite imagery was processed and analyzed (e.g Landsat TMETM+, Aster). The land cover regime was examined in detail by using sophisticated post-processing classification algorithms such as Maximum Likelihood, Parallelepiped Algorithm, Minimum Distance, Spectral Angle and Isodata. Texture features were calculated using the Grey Level Co-Occurence Matrix. In addition three classification techniques were compared : multispectral classification, texture based classification and a combination of both. The classification products were compared and evaluated for their accuracy. Moreover, a knowledge-rule method is proposed based on spectral, texture and shape features in order to create efficient land use and land cover maps of the study area. Morphometric parameters such as stream frequency, drainage density and elongation ratio were calculated in order to extract the basic watershed characteristics. In terms of the impacts of land use/cover on flooding, GIS and Fragstats tool were used to detect identifying trends, both visually and statistically, resulting from land use changes in a flood prone area such as Yialias by the use of spatial metrics. The results indicated that there is a considerable increase of urban areas cover during the period of the last 30 years. All these denoted that one of the main driving force of the increasing flood risk in catchment areas in Cyprus is generally associated to human activities.

Using Gaussian mixture models to detect and classify dolphin whistles and pulses.

PubMed

Peso Parada, Pablo; Cardenal-López, Antonio

2014-06-01

In recent years, a number of automatic detection systems for free-ranging cetaceans have been proposed that aim to detect not just surfaced, but also submerged, individuals. These systems are typically based on pattern-recognition techniques applied to underwater acoustic recordings. Using a Gaussian mixture model, a classification system was developed that detects sounds in recordings and classifies them as one of four types: background noise, whistles, pulses, and combined whistles and pulses. The classifier was tested using a database of underwater recordings made off the Spanish coast during 2011. Using cepstral-coefficient-based parameterization, a sound detection rate of 87.5% was achieved for a 23.6% classification error rate. To improve these results, two parameters computed using the multiple signal classification algorithm and an unpredictability measure were included in the classifier. These parameters, which helped to classify the segments containing whistles, increased the detection rate to 90.3% and reduced the classification error rate to 18.1%. Finally, the potential of the multiple signal classification algorithm and unpredictability measure for estimating whistle contours and classifying cetacean species was also explored, with promising results.

Quantitative evaluation of in vivo vital-dye fluorescence endoscopic imaging for the detection of Barrett’s-associated neoplasia

PubMed Central

Thekkek, Nadhi; Lee, Michelle H.; Polydorides, Alexandros D.; Rosen, Daniel G.; Anandasabapathy, Sharmila; Richards-Kortum, Rebecca

2015-01-01

Abstract. Current imaging tools are associated with inconsistent sensitivity and specificity for detection of Barrett’s-associated neoplasia. Optical imaging has shown promise in improving the classification of neoplasia in vivo. The goal of this pilot study was to evaluate whether in vivo vital dye fluorescence imaging (VFI) has the potential to improve the accuracy of early-detection of Barrett’s-associated neoplasia. In vivo endoscopic VFI images were collected from 65 sites in 14 patients with confirmed Barrett’s esophagus (BE), dysplasia, or esophageal adenocarcinoma using a modular video endoscope and a high-resolution microendoscope (HRME). Qualitative image features were compared to histology; VFI and HRME images show changes in glandular structure associated with neoplastic progression. Quantitative image features in VFI images were identified for objective image classification of metaplasia and neoplasia, and a diagnostic algorithm was developed using leave-one-out cross validation. Three image features extracted from VFI images were used to classify tissue as neoplastic or not with a sensitivity of 87.8% and a specificity of 77.6% (AUC=0.878). A multimodal approach incorporating VFI and HRME imaging can delineate epithelial changes present in Barrett’s-associated neoplasia. Quantitative analysis of VFI images may provide a means for objective interpretation of BE during surveillance. PMID:25950645

Quantitative evaluation of in vivo vital-dye fluorescence endoscopic imaging for the detection of Barrett's-associated neoplasia.

PubMed

Thekkek, Nadhi; Lee, Michelle H; Polydorides, Alexandros D; Rosen, Daniel G; Anandasabapathy, Sharmila; Richards-Kortum, Rebecca

2015-05-01

Current imaging tools are associated with inconsistent sensitivity and specificity for detection of Barrett's-associated neoplasia. Optical imaging has shown promise in improving the classification of neoplasia in vivo. The goal of this pilot study was to evaluate whether in vivo vital dye fluorescence imaging (VFI) has the potential to improve the accuracy of early-detection of Barrett's-associated neoplasia. In vivo endoscopic VFI images were collected from 65 sites in 14 patients with confirmed Barrett's esophagus (BE), dysplasia, oresophageal adenocarcinoma using a modular video endoscope and a high-resolution microendoscope(HRME). Qualitative image features were compared to histology; VFI and HRME images show changes in glandular structure associated with neoplastic progression. Quantitative image features in VFI images were identified for objective image classification of metaplasia and neoplasia, and a diagnostic algorithm was developed using leave-one-out cross validation. Three image features extracted from VFI images were used to classify tissue as neoplastic or not with a sensitivity of 87.8% and a specificity of 77.6% (AUC = 0.878). A multimodal approach incorporating VFI and HRME imaging can delineate epithelial changes present in Barrett's-associated neoplasia. Quantitative analysis of VFI images may provide a means for objective interpretation of BE during surveillance.

Spectral feature variations in x-ray diffraction imaging systems

NASA Astrophysics Data System (ADS)

Wolter, Scott D.; Greenberg, Joel A.

2016-05-01

Materials with different atomic or molecular structures give rise to unique scatter spectra when measured by X-ray diffraction. The details of these spectra, though, can vary based on both intrinsic (e.g., degree of crystallinity or doping) and extrinsic (e.g., pressure or temperature) conditions. While this sensitivity is useful for detailed characterizations of the material properties, these dependences make it difficult to perform more general classification tasks, such as explosives threat detection in aviation security. A number of challenges, therefore, currently exist for reliable substance detection including the similarity in spectral features among some categories of materials combined with spectral feature variations from materials processing and environmental factors. These factors complicate the creation of a material dictionary and the implementation of conventional classification and detection algorithms. Herein, we report on two prominent factors that lead to variations in spectral features: crystalline texture and temperature variations. Spectral feature comparisons between materials categories will be described for solid metallic sheet, aqueous liquids, polymer sheet, and metallic, organic, and inorganic powder specimens. While liquids are largely immune to texture effects, they are susceptible to temperature changes that can modify their density or produce phase changes. We will describe in situ temperature-dependent measurement of aqueous-based commercial goods in the temperature range of -20°C to 35°C.

Surgical treatment of adolescent internal condylar resorption (AICR) with articular disc repositioning and orthognathic surgery in the growing patient--a pilot study.

PubMed

Bodine, Trevor P; Wolford, Larry M; Araujo, Eustaquio; Oliver, Donald R; Buschang, Peter H

2016-01-01

The aim of this study was to better understand how surgical repositioning and stabilization of anteriorly displaced articular discs using the Mitek mini-anchor technique affects condylar growth in growing patients with adolescent internal condylar resorption (AICR). Twenty-two adolescent patients diagnosed with AICR and anterior temporomandibular disc displacement were compared to untreated control subjects without AICR matched for age, sex, and Angle classification. Pre-surgical (T1 and T2) and post-surgical (T3 and T4) mandibular tracings were superimposed on natural stable structures to evaluate the horizontal, vertical, and total changes in the position of condylion. The treated group showed an overall decrease in condylar height pre-surgically and statistically significant changes in condylar growth direction between the pre- and post-surgical observation periods. Pre-surgically, the treated group showed significantly more posterior condylar growth than the control group; they also showed inferior condylar growth, while the controls showed superior growth. Controls and patients in the treated group showed no significant differences in condylar growth post-surgically. Adolescent patients diagnosed with AICR and anterior disc displacement treated with mandibular ramus and maxillary osteotomies, along with Mitek anchors to reposition internally deranged discs, showed post-surgical normalization of condylar growth.

Error-related brain activity and error awareness in an error classification paradigm.

PubMed

Di Gregorio, Francesco; Steinhauser, Marco; Maier, Martin E

2016-10-01

Error-related brain activity has been linked to error detection enabling adaptive behavioral adjustments. However, it is still unclear which role error awareness plays in this process. Here, we show that the error-related negativity (Ne/ERN), an event-related potential reflecting early error monitoring, is dissociable from the degree of error awareness. Participants responded to a target while ignoring two different incongruent distractors. After responding, they indicated whether they had committed an error, and if so, whether they had responded to one or to the other distractor. This error classification paradigm allowed distinguishing partially aware errors, (i.e., errors that were noticed but misclassified) and fully aware errors (i.e., errors that were correctly classified). The Ne/ERN was larger for partially aware errors than for fully aware errors. Whereas this speaks against the idea that the Ne/ERN foreshadows the degree of error awareness, it confirms the prediction of a computational model, which relates the Ne/ERN to post-response conflict. This model predicts that stronger distractor processing - a prerequisite of error classification in our paradigm - leads to lower post-response conflict and thus a smaller Ne/ERN. This implies that the relationship between Ne/ERN and error awareness depends on how error awareness is related to response conflict in a specific task. Our results further indicate that the Ne/ERN but not the degree of error awareness determines adaptive performance adjustments. Taken together, we conclude that the Ne/ERN is dissociable from error awareness and foreshadows adaptive performance adjustments. Our results suggest that the relationship between the Ne/ERN and error awareness is correlative and mediated by response conflict. Copyright © 2016 Elsevier Inc. All rights reserved.

“How Did We Get Here?”: Topic Drift in Online Health Discussions

PubMed Central

Hartzler, Andrea L; Huh, Jina; Hsieh, Gary; McDonald, David W; Pratt, Wanda

2016-01-01

Background Patients increasingly use online health communities to exchange health information and peer support. During the progression of health discussions, a change of topic—topic drift—can occur. Topic drift is a frequent phenomenon linked to incoherence and frustration in online communities and other forms of computer-mediated communication. For sensitive topics, such as health, such drift could have life-altering repercussions, yet topic drift has not been studied in these contexts. Objective Our goals were to understand topic drift in online health communities and then to develop and evaluate an automated approach to detect both topic drift and efforts of community members to counteract such drift. Methods We manually analyzed 721 posts from 184 threads from 7 online health communities within WebMD to understand topic drift, members’ reaction towards topic drift, and their efforts to counteract topic drift. Then, we developed an automated approach to detect topic drift and counteraction efforts. We detected topic drift by calculating cosine similarity between 229,156 posts from 37,805 threads and measuring change of cosine similarity scores from the threads’ first posts to their sequential posts. Using a similar approach, we detected counteractions to topic drift in threads by focusing on the irregular increase of similarity scores compared to the previous post in threads. Finally, we evaluated the performance of our automated approaches to detect topic drift and counteracting efforts by using a manually developed gold standard. Results Our qualitative analyses revealed that in threads of online health communities, topics change gradually, but usually stay within the global frame of topics for the specific community. Members showed frustration when topic drift occurred in the middle of threads but reacted positively to off-topic stories shared as separate threads. Although all types of members helped to counteract topic drift, original posters provided the most effort to keep threads on topic. Cosine similarity scores show promise for automatically detecting topical changes in online health discussions. In our manual evaluation, we achieved an F1 score of .71 and .73 for detecting topic drift and counteracting efforts to stay on topic, respectively. Conclusions Our analyses expand our understanding of topic drift in a health context and highlight practical implications, such as promoting off-topic discussions as a function of building rapport in online health communities. Furthermore, the quantitative findings suggest that an automated tool could help detect topic drift, support counteraction efforts to bring the conversation back on topic, and improve communication in these important communities. Findings from this study have the potential to reduce topic drift and improve online health community members’ experience of computer-mediated communication. Improved communication could enhance the personal health management of members who seek essential information and support during times of difficulty. PMID:27806924

Oil palm plantation effects on water quality in Kalimantan, Indonesia

NASA Astrophysics Data System (ADS)

Carlson, K. M.; Curran, L. M.

2011-12-01

Global demand for palm oil has stimulated a 7-fold increase in oil palm (Elaeis guineensis) plantation area in Indonesia since 1990. Expansion will continue as Indonesia plans to double current production by 2020. Oil palm fertilizers, effluent from oil palm mills, and erosion from land clearing and roads threaten river water quality near plantations. These rivers provide essential ecosystem services including water for drinking, cooking, and washing. Robust empirical measurements of plantation expansion impacts on water resources are necessary to discern the effects of agribusiness on local livelihoods and ecosystems. In Ketapang District, West Kalimantan, Indonesian Borneo, we evaluated the effects of land cover change on water quality by assessing water chemistry in streams draining four end-member watersheds ( ~600-1900 ha watershed-1): Logged forest, mixed agro-forest dominated by rubber and upland rice fallows, young oil palm forest (0-5 years), and old oil palm forest (10-15 years). To assess land cover change, we used CLASLite software to derive fractional cover from a time series (1989-2008) of Landsat data. Nearest neighbor classification and post-classification change detection yielded classes including primary forest, logged forest, secondary forest regrowth, smallholder agriculture, and oil palm. Stream water quality (temperature, dissolved oxygen, turbidity, optical chlorphyll, and pH) and quantity (discharge) were quantified with the YSI 6600-V2 sonde. The sonde was deployed in each stream for month-long intervals 2-3 times from 2009-2010. Such extended deployment captures episodic events such as intense storms and allows examination of interdiel dynamics by sampling continuously and at high frequency, every 10 minutes. We find that across the Ketapang District study region (~12,000 km2), oil palm has cleared mostly forests (49%) and agroforests (39%). What are the impacts of such land cover changes on water quality? Compared to forests and agroforests, streams draining oil palm show greater biological activity, as indicated by elevated pH and reduced dissolved oxygen levels. Moreover, turbidity is elevated in young oil palm plantations watersheds compared to forest, agroforest, and old oil palm land covers. We discuss the implications of these findings for communities and ecosystems.

Analysis on Target Detection and Classification in LTE Based Passive Forward Scattering Radar.

PubMed

Raja Abdullah, Raja Syamsul Azmir; Abdul Aziz, Noor Hafizah; Abdul Rashid, Nur Emileen; Ahmad Salah, Asem; Hashim, Fazirulhisyam

2016-09-29

The passive bistatic radar (PBR) system can utilize the illuminator of opportunity to enhance radar capability. By utilizing the forward scattering technique and procedure into the specific mode of PBR can provide an improvement in target detection and classification. The system is known as passive Forward Scattering Radar (FSR). The passive FSR system can exploit the peculiar advantage of the enhancement in forward scatter radar cross section (FSRCS) for target detection. Thus, the aim of this paper is to show the feasibility of passive FSR for moving target detection and classification by experimental analysis and results. The signal source is coming from the latest technology of 4G Long-Term Evolution (LTE) base station. A detailed explanation on the passive FSR receiver circuit, the detection scheme and the classification algorithm are given. In addition, the proposed passive FSR circuit employs the self-mixing technique at the receiver; hence the synchronization signal from the transmitter is not required. The experimental results confirm the passive FSR system's capability for ground target detection and classification. Furthermore, this paper illustrates the first classification result in the passive FSR system. The great potential in the passive FSR system provides a new research area in passive radar that can be used for diverse remote monitoring applications.

Analysis on Target Detection and Classification in LTE Based Passive Forward Scattering Radar

PubMed Central

Raja Abdullah, Raja Syamsul Azmir; Abdul Aziz, Noor Hafizah; Abdul Rashid, Nur Emileen; Ahmad Salah, Asem; Hashim, Fazirulhisyam

2016-01-01

The passive bistatic radar (PBR) system can utilize the illuminator of opportunity to enhance radar capability. By utilizing the forward scattering technique and procedure into the specific mode of PBR can provide an improvement in target detection and classification. The system is known as passive Forward Scattering Radar (FSR). The passive FSR system can exploit the peculiar advantage of the enhancement in forward scatter radar cross section (FSRCS) for target detection. Thus, the aim of this paper is to show the feasibility of passive FSR for moving target detection and classification by experimental analysis and results. The signal source is coming from the latest technology of 4G Long-Term Evolution (LTE) base station. A detailed explanation on the passive FSR receiver circuit, the detection scheme and the classification algorithm are given. In addition, the proposed passive FSR circuit employs the self-mixing technique at the receiver; hence the synchronization signal from the transmitter is not required. The experimental results confirm the passive FSR system’s capability for ground target detection and classification. Furthermore, this paper illustrates the first classification result in the passive FSR system. The great potential in the passive FSR system provides a new research area in passive radar that can be used for diverse remote monitoring applications. PMID:27690051

Long-term prediction of changes in health status, frailty, nursing care and mortality in community-dwelling senior citizens—results from the Longitudinal Urban Cohort Ageing Study (LUCAS).

PubMed

Dapp, Ulrike; Minder, Christoph E; Anders, Jennifer; Golgert, Stefan; von Renteln-Kruse, Wolfgang

2014-12-19

The detection of incipient functional decline in elderly persons is not an easy task. Here, we propose the self-reporting Functional Ability Index (FA index) suitable to screen functional competence in senior citizens in the community setting. Its prognostic validity was investigated in the Longitudinal Urban Cohort Ageing Study (LUCAS). This index is based equally on both, resources and risks/functional restrictions which precede ADL limitations. Since 2001, the FA index was tested in the LUCAS cohort without any ADL restrictions at baseline (n = 1,679), and followed up by repeated questionnaires in Hamburg, Germany. Applying the index, 1,022 LUCAS participants were initially classified as Robust (60.9%), 220 as postRobust (13.1%), 172 as preFrail (10.2%) and 265 as Frail (15.8%). This classification correlated with self-reported health, chronic pain and depressive mood (rank correlations 0.42, 0.26, 0.21; all p < .0001). Survival analyses showed significant differences between these classes as determined by the FA index: the initially Robust survived longest, the Frail shortest (p < .0001). Analyses of the time to need of nursing care revealed similar results. Significant differences persisted after adjustment for age, sex and self-reported health. Disability free lifetime and its development over time are important topics in public health. In this context, the FA index presented here provides answers to two questions. First, how to screen the heterogeneous population of community-dwelling senior citizens, i.e. for their functional ability/competence, and second, how far away they are from disability/dependency. Furthermore, the index provides a tool to address the urgent question whether incipient functional decline/incipient frailty can be recognized early to be influenced positively. The FA index predicted change in functional status, future need of nursing care, and mortality in an unselected population of community-dwelling seniors. It implies an operational specification of the classification into Robust, postRobust, preFrail and Frail. Based on a self-administered questionnaire, the FA index allows easy screening of elderly persons for declining functional competence. Thereby, incipient functional decline is recognized, e.g. in GPs' practices and senior community health centers, to initiate early appropriate preventive action.

Post-disaster assessment of landslides in southern Taiwan after 2009 Typhoon Morakot using remote sensing and spatial analysis

NASA Astrophysics Data System (ADS)

Tsai, F.; Hwang, J.-H.; Chen, L.-C.; Lin, T.-H.

2010-10-01

On 8 August 2009, the extreme rainfall of Typhoon Morakot triggered enormous landslides in mountainous regions of southern Taiwan, causing catastrophic infrastructure and property damages and human casualties. A comprehensive evaluation of the landslides is essential for the post-disaster reconstruction and should be helpful for future hazard mitigation. This paper presents a systematic approach to utilize multi-temporal satellite images and other geo-spatial data for the post-disaster assessment of landslides on a regional scale. Rigorous orthorectification and radiometric correction procedures were applied to the satellite images. Landslides were identified with NDVI filtering, change detection analysis and interactive post-analysis editing to produce an accurate landslide map. Spatial analysis was performed to obtain statistical characteristics of the identified landslides and their relationship with topographical factors. A total of 9333 landslides (22 590 ha) was detected from change detection analysis of satellite images. Most of the detected landslides are smaller than 10 ha. Less than 5% of them are larger than 10 ha but together they constitute more than 45% of the total landslide area. Spatial analysis of the detected landslides indicates that most of them have average elevations between 500 m to 2000 m and with average slope gradients between 20° and 40°. In addition, a particularly devastating landslide whose debris flow destroyed a riverside village was examined in depth for detailed investigation. The volume of this slide is estimated to be more than 2.6 million m3 with an average depth of 40 m.

Multiple kernel based feature and decision level fusion of iECO individuals for explosive hazard detection in FLIR imagery

NASA Astrophysics Data System (ADS)

Price, Stanton R.; Murray, Bryce; Hu, Lequn; Anderson, Derek T.; Havens, Timothy C.; Luke, Robert H.; Keller, James M.

2016-05-01

A serious threat to civilians and soldiers is buried and above ground explosive hazards. The automatic detection of such threats is highly desired. Many methods exist for explosive hazard detection, e.g., hand-held based sensors, downward and forward looking vehicle mounted platforms, etc. In addition, multiple sensors are used to tackle this extreme problem, such as radar and infrared (IR) imagery. In this article, we explore the utility of feature and decision level fusion of learned features for forward looking explosive hazard detection in IR imagery. Specifically, we investigate different ways to fuse learned iECO features pre and post multiple kernel (MK) support vector machine (SVM) based classification. Three MK strategies are explored; fixed rule, heuristics and optimization-based. Performance is assessed in the context of receiver operating characteristic (ROC) curves on data from a U.S. Army test site that contains multiple target and clutter types, burial depths and times of day. Specifically, the results reveal two interesting things. First, the different MK strategies appear to indicate that the different iECO individuals are all more-or-less important and there is not a dominant feature. This is reinforcing as our hypothesis was that iECO provides different ways to approach target detection. Last, we observe that while optimization-based MK is mathematically appealing, i.e., it connects the learning of the fusion to the underlying classification problem we are trying to solve, it appears to be highly susceptible to over fitting and simpler, e.g., fixed rule and heuristics approaches help us realize more generalizable iECO solutions.

75 FR 70754 - Postal Classification Changes

Federal Register 2010, 2011, 2012, 2013, 2014

2010-11-18

... POSTAL REGULATORY COMMISSION [Docket No. MC2011-5; Order No. 583] Postal Classification Changes...-filed Postal Service request announcing a classification change affecting bundle and container charges... Commission announcing a classification change [[Page 70755

International standards for neurological classification of spinal cord injury: impact of the revised worksheet (revision 02/13) on classification performance.

PubMed

Schuld, Christian; Franz, Steffen; Brüggemann, Karin; Heutehaus, Laura; Weidner, Norbert; Kirshblum, Steven C; Rupp, Rüdiger

2016-09-01

Prospective cohort study. Comparison of the classification performance between the worksheet revisions of 2011 and 2013 of the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI). Ongoing ISNCSCI instructional courses of the European Multicenter Study on Human Spinal Cord Injury (EMSCI). For quality control all participants were requested to classify five ISNCSCI cases directly before (pre-test) and after (post-test) the workshop. One hundred twenty-five clinicians working in 22 SCI centers attended the instructional course between November 2011 and March 2015. Seventy-two clinicians completed the post-test with the 2011 revision of the worksheet and 53 with the 2013 revision. Not applicable. The clinicians' classification performance assessed by the percentage of correctly determined motor levels (ML) and sensory levels, neurological levels of injury (NLI), ASIA Impairment Scales and zones of partial preservations. While no group differences were found in the pre-tests, the overall performance (rev2011: 92.2% ± 6.7%, rev2013: 94.3% ± 7.7%; P = 0.010), the percentage of correct MLs (83.2% ± 14.5% vs. 88.1% ± 15.3%; P = 0.046) and NLIs (86.1% ± 16.7% vs. 90.9% ± 18.6%; P = 0.043) improved significantly in the post-tests. Detailed ML analysis revealed the largest benefit of the 2013 revision (50.0% vs. 67.0%) in a case with a high cervical injury (NLI C2). The results from the EMSCI ISNCSCI post-tests show a significantly better classification performance using the revised 2013 worksheet presumably due to the body-side based grouping of myotomes and dermatomes and their correct horizontal alignment. Even with these proven advantages of the new layout, the correct determination of MLs in the segments C2-C4 remains difficult.

Improving the performance of univariate control charts for abnormal detection and classification

NASA Astrophysics Data System (ADS)

Yiakopoulos, Christos; Koutsoudaki, Maria; Gryllias, Konstantinos; Antoniadis, Ioannis

2017-03-01

Bearing failures in rotating machinery can cause machine breakdown and economical loss, if no effective actions are taken on time. Therefore, it is of prime importance to detect accurately the presence of faults, especially at their early stage, to prevent sequent damage and reduce costly downtime. The machinery fault diagnosis follows a roadmap of data acquisition, feature extraction and diagnostic decision making, in which mechanical vibration fault feature extraction is the foundation and the key to obtain an accurate diagnostic result. A challenge in this area is the selection of the most sensitive features for various types of fault, especially when the characteristics of failures are difficult to be extracted. Thus, a plethora of complex data-driven fault diagnosis methods are fed by prominent features, which are extracted and reduced through traditional or modern algorithms. Since most of the available datasets are captured during normal operating conditions, the last decade a number of novelty detection methods, able to work when only normal data are available, have been developed. In this study, a hybrid method combining univariate control charts and a feature extraction scheme is introduced focusing towards an abnormal change detection and classification, under the assumption that measurements under normal operating conditions of the machinery are available. The feature extraction method integrates the morphological operators and the Morlet wavelets. The effectiveness of the proposed methodology is validated on two different experimental cases with bearing faults, demonstrating that the proposed approach can improve the fault detection and classification performance of conventional control charts.

Shape asymmetry: a morphological indicator for automatic detection of galaxies in the post-coalescence merger stages

NASA Astrophysics Data System (ADS)

Pawlik, M. M.; Wild, V.; Walcher, C. J.; Johansson, P. H.; Villforth, C.; Rowlands, K.; Mendez-Abreu, J.; Hewlett, T.

2016-03-01

We present a new morphological indicator designed for automated recognition of galaxies with faint asymmetric tidal features suggestive of an ongoing or past merger. We use the new indicator, together with pre-existing diagnostics of galaxy structure to study the role of galaxy mergers in inducing (post-) starburst spectral signatures in local galaxies, and investigate whether (post-) starburst galaxies play a role in the build-up of the `red sequence'. Our morphological and structural analysis of an evolutionary sample of 335 (post-) starburst galaxies in the Sloan Digital Sky Survey DR7 with starburst ages 0 < tSB < 0.6 Gyr, shows that 45 per cent of galaxies with young starbursts (tSB < 0.1 Gyr) show signatures of an ongoing or past merger. This fraction declines with starburst age, and we find a good agreement between automated and visual classifications. The majority of the oldest (post-) starburst galaxies in our sample (tSB ˜ 0.6 Gyr) have structural properties characteristic of early-type discs and are not as highly concentrated as the fully quenched galaxies commonly found on the `red sequence' in the present day Universe. This suggests that, if (post-) starburst galaxies are a transition phase between active star-formation and quiescence, they do not attain the structure of presently quenched galaxies within the first 0.6 Gyr after the starburst.

Epstein-Barr Virus–Associated Lymphoproliferative Disorders: Review and Update on 2016 WHO Classification

PubMed Central

Kim, Hyun-Jung; Ko, Young Hyeh; Kim, Ji Eun; Lee, Seung-Sook; Lee, Hyekyung; Park, Gyeongsin; Paik, Jin Ho; Cha, Hee Jeong; Choi, Yoo-Duk; Han, Jae Ho; Huh, Jooryung

2017-01-01

Epstein-Barr virus (human herpesvirus-4) is very common virus that can be detected in more than 95% of the human population. Most people are asymptomatic and live their entire lives in a chronically infected state (IgG positive). However, in some populations, the Epstein-Barr virus (EBV) has been involved in the occurrence of a wide range of B-cell lymphoproliferative disorders (LPDs), including Burkitt lymphoma, classic Hodgkin’s lymphoma, and immune–deficiency associated LPDs (post-transplant and human immunodeficiency virus–associated LPDs). T-cell LPDs have been reported to be associated with EBV with a subset of peripheral T-cell lymphomas, angioimmunoblastic T-cell lymphomas, extranodal nasal natural killer/T-cell lymphomas, and other rare histotypes. This article reviews the current evidence covering EBV-associated LPDs based on the 2016 classification of the World Health Organization. These LPD entities often pose diagnostic challenges, both clinically and pathologically, so it is important to understand their unique pathophysiology for correct diagnoses and optimal management. PMID:28592786

Saliency-Guided Change Detection of Remotely Sensed Images Using Random Forest

NASA Astrophysics Data System (ADS)

Feng, W.; Sui, H.; Chen, X.

2018-04-01

Studies based on object-based image analysis (OBIA) representing the paradigm shift in change detection (CD) have achieved remarkable progress in the last decade. Their aim has been developing more intelligent interpretation analysis methods in the future. The prediction effect and performance stability of random forest (RF), as a new kind of machine learning algorithm, are better than many single predictors and integrated forecasting method. In this paper, we present a novel CD approach for high-resolution remote sensing images, which incorporates visual saliency and RF. First, highly homogeneous and compact image super-pixels are generated using super-pixel segmentation, and the optimal segmentation result is obtained through image superimposition and principal component analysis (PCA). Second, saliency detection is used to guide the search of interest regions in the initial difference image obtained via the improved robust change vector analysis (RCVA) algorithm. The salient regions within the difference image that correspond to the binarized saliency map are extracted, and the regions are subject to the fuzzy c-means (FCM) clustering to obtain the pixel-level pre-classification result, which can be used as a prerequisite for superpixel-based analysis. Third, on the basis of the optimal segmentation and pixel-level pre-classification results, different super-pixel change possibilities are calculated. Furthermore, the changed and unchanged super-pixels that serve as the training samples are automatically selected. The spectral features and Gabor features of each super-pixel are extracted. Finally, superpixel-based CD is implemented by applying RF based on these samples. Experimental results on Ziyuan 3 (ZY3) multi-spectral images show that the proposed method outperforms the compared methods in the accuracy of CD, and also confirm the feasibility and effectiveness of the proposed approach.

Video segmentation for post-production

NASA Astrophysics Data System (ADS)

Wills, Ciaran

2001-12-01

Specialist post-production is an industry that has much to gain from the application of content-based video analysis techniques. However the types of material handled in specialist post-production, such as television commercials, pop music videos and special effects are quite different in nature from the typical broadcast material which many video analysis techniques are designed to work with; shots are short and highly dynamic, and the transitions are often novel or ambiguous. We address the problem of scene change detection and develop a new algorithm which tackles some of the common aspects of post-production material that cause difficulties for past algorithms, such as illumination changes and jump cuts. Operating in the compressed domain on Motion JPEG compressed video, our algorithm detects cuts and fades by analyzing each JPEG macroblock in the context of its temporal and spatial neighbors. Analyzing the DCT coefficients directly we can extract the mean color of a block and an approximate detail level. We can also perform an approximated cross-correlation between two blocks. The algorithm is part of a set of tools being developed to work with an automated asset management system designed specifically for use in post-production facilities.

Change detection of medical images using dictionary learning techniques and PCA

NASA Astrophysics Data System (ADS)

Nika, Varvara; Babyn, Paul; Zhu, Hongmei

2014-03-01

Automatic change detection methods for identifying the changes of serial MR images taken at different times are of great interest to radiologists. The majority of existing change detection methods in medical imaging, and those of brain images in particular, include many preprocessing steps and rely mostly on statistical analysis of MRI scans. Although most methods utilize registration software, tissue classification remains a difficult and overwhelming task. Recently, dictionary learning techniques are used in many areas of image processing, such as image surveillance, face recognition, remote sensing, and medical imaging. In this paper we present the Eigen-Block Change Detection algorithm (EigenBlockCD). It performs local registration and identifies the changes between consecutive MR images of the brain. Blocks of pixels from baseline scan are used to train local dictionaries that are then used to detect changes in the follow-up scan. We use PCA to reduce the dimensionality of the local dictionaries and the redundancy of data. Choosing the appropriate distance measure significantly affects the performance of our algorithm. We examine the differences between L1 and L2 norms as two possible similarity measures in the EigenBlockCD. We show the advantages of L2 norm over L1 norm theoretically and numerically. We also demonstrate the performance of the EigenBlockCD algorithm for detecting changes of MR images and compare our results with those provided in recent literature. Experimental results with both simulated and real MRI scans show that the EigenBlockCD outperforms the previous methods. It detects clinical changes while ignoring the changes due to patient's position and other acquisition artifacts.

Comparing the utility of image algebra operations for characterizing landscape changes: the case of the Mediterranean coast.

PubMed

Alphan, Hakan

2011-11-01

The aim of this study is to compare various image algebra procedures for their efficiency in locating and identifying different types of landscape changes on the margin of a Mediterranean coastal plain, Cukurova, Turkey. Image differencing and ratioing were applied to the reflective bands of Landsat TM datasets acquired in 1984 and 2006. Normalized Difference Vegetation index (NDVI) and Principal Component Analysis (PCA) differencing were also applied. The resulting images were tested for their capacity to detect nine change phenomena, which were a priori defined in a three-level classification scheme. These change phenomena included agricultural encroachment, sand dune afforestation, coastline changes and removal/expansion of reed beds. The percentage overall accuracies of different algebra products for each phenomenon were calculated and compared. The results showed that some of the changes such as sand dune afforestation and reed bed expansion were detected with accuracies varying between 85 and 97% by the majority of the algebra operations, while some other changes such as logging could only be detected by mid-infrared (MIR) ratioing. For optimizing change detection in similar coastal landscapes, underlying causes of these changes were discussed and the guidelines for selecting band and algebra operations were provided. Copyright © 2011 Elsevier Ltd. All rights reserved.

Performance assessment of automated tissue characterization for prostate H and E stained histopathology

NASA Astrophysics Data System (ADS)

DiFranco, Matthew D.; Reynolds, Hayley M.; Mitchell, Catherine; Williams, Scott; Allan, Prue; Haworth, Annette

2015-03-01

Reliable automated prostate tumor detection and characterization in whole-mount histology images is sought in many applications, including post-resection tumor staging and as ground-truth data for multi-parametric MRI interpretation. In this study, an ensemble-based supervised classification algorithm for high-resolution histology images was trained on tile-based image features including histogram and gray-level co-occurrence statistics. The algorithm was assessed using different combinations of H and E prostate slides from two separate medical centers and at two different magnifications (400x and 200x), with the aim of applying tumor classification models to new data. Slides from both datasets were annotated by expert pathologists in order to identify homogeneous cancerous and non-cancerous tissue regions of interest, which were then categorized as (1) low-grade tumor (LG-PCa), including Gleason 3 and high-grade prostatic intraepithelial neoplasia (HG-PIN), (2) high-grade tumor (HG-PCa), including various Gleason 4 and 5 patterns, or (3) non-cancerous, including benign stroma and benign prostatic hyperplasia (BPH). Classification models for both LG-PCa and HG-PCa were separately trained using a support vector machine (SVM) approach, and per-tile tumor prediction maps were generated from the resulting ensembles. Results showed high sensitivity for predicting HG-PCa with an AUC up to 0.822 using training data from both medical centres, while LG-PCa showed a lower sensitivity of 0.763 with the same training data. Visual inspection of cancer probability heatmaps from 9 patients showed that 17/19 tumors were detected, and HG-PCa generally reported less false positives than LG-PCa.

ECG signal analysis through hidden Markov models.

PubMed

Andreão, Rodrigo V; Dorizzi, Bernadette; Boudy, Jérôme

2006-08-01

This paper presents an original hidden Markov model (HMM) approach for online beat segmentation and classification of electrocardiograms. The HMM framework has been visited because of its ability of beat detection, segmentation and classification, highly suitable to the electrocardiogram (ECG) problem. Our approach addresses a large panel of topics some of them never studied before in other HMM related works: waveforms modeling, multichannel beat segmentation and classification, and unsupervised adaptation to the patient's ECG. The performance was evaluated on the two-channel QT database in terms of waveform segmentation precision, beat detection and classification. Our waveform segmentation results compare favorably to other systems in the literature. We also obtained high beat detection performance with sensitivity of 99.79% and a positive predictivity of 99.96%, using a test set of 59 recordings. Moreover, premature ventricular contraction beats were detected using an original classification strategy. The results obtained validate our approach for real world application.

Continuous robust sound event classification using time-frequency features and deep learning

PubMed Central

Song, Yan; Xiao, Wei; Phan, Huy

2017-01-01

The automatic detection and recognition of sound events by computers is a requirement for a number of emerging sensing and human computer interaction technologies. Recent advances in this field have been achieved by machine learning classifiers working in conjunction with time-frequency feature representations. This combination has achieved excellent accuracy for classification of discrete sounds. The ability to recognise sounds under real-world noisy conditions, called robust sound event classification, is an especially challenging task that has attracted recent research attention. Another aspect of real-word conditions is the classification of continuous, occluded or overlapping sounds, rather than classification of short isolated sound recordings. This paper addresses the classification of noise-corrupted, occluded, overlapped, continuous sound recordings. It first proposes a standard evaluation task for such sounds based upon a common existing method for evaluating isolated sound classification. It then benchmarks several high performing isolated sound classifiers to operate with continuous sound data by incorporating an energy-based event detection front end. Results are reported for each tested system using the new task, to provide the first analysis of their performance for continuous sound event detection. In addition it proposes and evaluates a novel Bayesian-inspired front end for the segmentation and detection of continuous sound recordings prior to classification. PMID:28892478

Continuous robust sound event classification using time-frequency features and deep learning.

PubMed

McLoughlin, Ian; Zhang, Haomin; Xie, Zhipeng; Song, Yan; Xiao, Wei; Phan, Huy

2017-01-01

The automatic detection and recognition of sound events by computers is a requirement for a number of emerging sensing and human computer interaction technologies. Recent advances in this field have been achieved by machine learning classifiers working in conjunction with time-frequency feature representations. This combination has achieved excellent accuracy for classification of discrete sounds. The ability to recognise sounds under real-world noisy conditions, called robust sound event classification, is an especially challenging task that has attracted recent research attention. Another aspect of real-word conditions is the classification of continuous, occluded or overlapping sounds, rather than classification of short isolated sound recordings. This paper addresses the classification of noise-corrupted, occluded, overlapped, continuous sound recordings. It first proposes a standard evaluation task for such sounds based upon a common existing method for evaluating isolated sound classification. It then benchmarks several high performing isolated sound classifiers to operate with continuous sound data by incorporating an energy-based event detection front end. Results are reported for each tested system using the new task, to provide the first analysis of their performance for continuous sound event detection. In addition it proposes and evaluates a novel Bayesian-inspired front end for the segmentation and detection of continuous sound recordings prior to classification.

Reducing uncertainty on satellite image classification through spatiotemporal reasoning

NASA Astrophysics Data System (ADS)

Partsinevelos, Panagiotis; Nikolakaki, Natassa; Psillakis, Periklis; Miliaresis, George; Xanthakis, Michail

2014-05-01

The natural habitat constantly endures both inherent natural and human-induced influences. Remote sensing has been providing monitoring oriented solutions regarding the natural Earth surface, by offering a series of tools and methodologies which contribute to prudent environmental management. Processing and analysis of multi-temporal satellite images for the observation of the land changes include often classification and change-detection techniques. These error prone procedures are influenced mainly by the distinctive characteristics of the study areas, the remote sensing systems limitations and the image analysis processes. The present study takes advantage of the temporal continuity of multi-temporal classified images, in order to reduce classification uncertainty, based on reasoning rules. More specifically, pixel groups that temporally oscillate between classes are liable to misclassification or indicate problematic areas. On the other hand, constant pixel group growth indicates a pressure prone area. Computational tools are developed in order to disclose the alterations in land use dynamics and offer a spatial reference to the pressures that land use classes endure and impose between them. Moreover, by revealing areas that are susceptible to misclassification, we propose specific target site selection for training during the process of supervised classification. The underlying objective is to contribute to the understanding and analysis of anthropogenic and environmental factors that influence land use changes. The developed algorithms have been tested upon Landsat satellite image time series, depicting the National Park of Ainos in Kefallinia, Greece, where the unique in the world Abies cephalonica grows. Along with the minor changes and pressures indicated in the test area due to harvesting and other human interventions, the developed algorithms successfully captured fire incidents that have been historically confirmed. Overall, the results have shown that the use of the suggested procedures can contribute to the reduction of the classification uncertainty and support the existing knowledge regarding the pressure among land-use changes.

Evaluation of post-surgical cognitive function and protein fingerprints in the cerebro-spinal fluid utilizing surface-enhanced laser Desorption/Ionization time-of-flight mass-spectrometry (SELDI-TOF MS) after coronary artery bypass grafting: review of proteomic analytic tools and introducing a new syndrome.

PubMed

Reis, H J; Wang, L; Verano-Braga, T; Pimenta, A M C; Kálmán, J; Bogáts, G; Babik, B; Vieira, L B; Teixeira, A L; Mukhamedyarov, M A; Zefirov, A L; Kiyasov, A P; Rizvanov, A A; Matin, K; Palotás, M; Guimarães, M M; Ferreira, C N; Yalvaç, M E; Janka, Z; Palotás, A

2011-01-01

Cognitive dysfunction following surgery is a common complication, which increases the incidence of other co-morbid conditions, hospital and health-care costs. The reported rate of the occurrence of post-operative cognitive decline varies with different studies, depending on population profile, type of surgery, definition of cognitive disorder and detection methods, design of study, etc. It remains unclear whether these psychiatric signs and symptoms are direct results of the effects of surgery or general anesthesia. Nonetheless they are more frequent after cardiac surgery and are likely to be multi-factorial, but the patho-mechanisms are not yet fully characterized. This communication provides a synopsis of proteomics tools and delineates novel SELDI-TOF results to evaluate biomarkers in this regard. Presented for the first time is a classification of the clinically relevant forms of post-operative cognitive decline with the advent of a novel subclass.

Robust skin color-based moving object detection for video surveillance

NASA Astrophysics Data System (ADS)

Kaliraj, Kalirajan; Manimaran, Sudha

2016-07-01

Robust skin color-based moving object detection for video surveillance is proposed. The objective of the proposed algorithm is to detect and track the target under complex situations. The proposed framework comprises four stages, which include preprocessing, skin color-based feature detection, feature classification, and target localization and tracking. In the preprocessing stage, the input image frame is smoothed using averaging filter and transformed into YCrCb color space. In skin color detection, skin color regions are detected using Otsu's method of global thresholding. In the feature classification, histograms of both skin and nonskin regions are constructed and the features are classified into foregrounds and backgrounds based on Bayesian skin color classifier. The foreground skin regions are localized by a connected component labeling process. Finally, the localized foreground skin regions are confirmed as a target by verifying the region properties, and nontarget regions are rejected using the Euler method. At last, the target is tracked by enclosing the bounding box around the target region in all video frames. The experiment was conducted on various publicly available data sets and the performance was evaluated with baseline methods. It evidently shows that the proposed algorithm works well against slowly varying illumination, target rotations, scaling, fast, and abrupt motion changes.

Class imbalance in unsupervised change detection - A diagnostic analysis from urban remote sensing

NASA Astrophysics Data System (ADS)

Leichtle, Tobias; Geiß, Christian; Lakes, Tobia; Taubenböck, Hannes

2017-08-01

Automatic monitoring of changes on the Earth's surface is an intrinsic capability and simultaneously a persistent methodological challenge in remote sensing, especially regarding imagery with very-high spatial resolution (VHR) and complex urban environments. In order to enable a high level of automatization, the change detection problem is solved in an unsupervised way to alleviate efforts associated with collection of properly encoded prior knowledge. In this context, this paper systematically investigates the nature and effects of class distribution and class imbalance in an unsupervised binary change detection application based on VHR imagery over urban areas. For this purpose, a diagnostic framework for sensitivity analysis of a large range of possible degrees of class imbalance is presented, which is of particular importance with respect to unsupervised approaches where the content of images and thus the occurrence and the distribution of classes are generally unknown a priori. Furthermore, this framework can serve as a general technique to evaluate model transferability in any two-class classification problem. The applied change detection approach is based on object-based difference features calculated from VHR imagery and subsequent unsupervised two-class clustering using k-means, genetic k-means and self-organizing map (SOM) clustering. The results from two test sites with different structural characteristics of the built environment demonstrated that classification performance is generally worse in imbalanced class distribution settings while best results were reached in balanced or close to balanced situations. Regarding suitable accuracy measures for evaluating model performance in imbalanced settings, this study revealed that the Kappa statistics show significant response to class distribution while the true skill statistic was widely insensitive to imbalanced classes. In general, the genetic k-means clustering algorithm achieved the most robust results with respect to class imbalance while the SOM clustering exhibited a distinct optimization towards a balanced distribution of classes.

Object-Oriented Analysis of Satellite Images Using Artificial Neural Networks for Post-Earthquake Buildings Change Detection

NASA Astrophysics Data System (ADS)

Khodaverdi zahraee, N.; Rastiveis, H.

2017-09-01

Earthquake is one of the most divesting natural events that threaten human life during history. After the earthquake, having information about the damaged area, the amount and type of damage can be a great help in the relief and reconstruction for disaster managers. It is very important that these measures should be taken immediately after the earthquake because any negligence could be more criminal losses. The purpose of this paper is to propose and implement an automatic approach for mapping destructed buildings after an earthquake using pre- and post-event high resolution satellite images. In the proposed method after preprocessing, segmentation of both images is performed using multi-resolution segmentation technique. Then, the segmentation results are intersected with ArcGIS to obtain equal image objects on both images. After that, appropriate textural features, which make a better difference between changed or unchanged areas, are calculated for all the image objects. Finally, subtracting the extracted textural features from pre- and post-event images, obtained values are applied as an input feature vector in an artificial neural network for classifying the area into two classes of changed and unchanged areas. The proposed method was evaluated using WorldView2 satellite images, acquired before and after the 2010 Haiti earthquake. The reported overall accuracy of 93% proved the ability of the proposed method for post-earthquake buildings change detection.

A Review of the Quantification and Classification of Pigmented Skin Lesions: From Dedicated to Hand-Held Devices.

PubMed

Filho, Mercedes; Ma, Zhen; Tavares, João Manuel R S

2015-11-01

In recent years, the incidence of skin cancer cases has risen, worldwide, mainly due to the prolonged exposure to harmful ultraviolet radiation. Concurrently, the computer-assisted medical diagnosis of skin cancer has undergone major advances, through an improvement in the instrument and detection technology, and the development of algorithms to process the information. Moreover, because there has been an increased need to store medical data, for monitoring, comparative and assisted-learning purposes, algorithms for data processing and storage have also become more efficient in handling the increase of data. In addition, the potential use of common mobile devices to register high-resolution images of skin lesions has also fueled the need to create real-time processing algorithms that may provide a likelihood for the development of malignancy. This last possibility allows even non-specialists to monitor and follow-up suspected skin cancer cases. In this review, we present the major steps in the pre-processing, processing and post-processing of skin lesion images, with a particular emphasis on the quantification and classification of pigmented skin lesions. We further review and outline the future challenges for the creation of minimum-feature, automated and real-time algorithms for the detection of skin cancer from images acquired via common mobile devices.

Validity of the prenatal risk overview for detecting drug use disorders in pregnancy.

PubMed

Harrison, Patricia A; Godecker, Amy; Sidebottom, Abbey

2012-11-01

To validate the Prenatal Risk Overview (PRO) drug use questions against a structured diagnostic interview among pregnant women. Prenatal care patients were administered the PRO at intake and then asked to consent to a research diagnostic interview. Of 1,367 women asked to participate, 1,274 consented and 745 completed the study. Three drug use items comprised one of 13 PRO psychosocial risk domains. The Structured Clinical Interview for DSM-IV (SCID) was used as the validation instrument. To assess criterion validity, the Moderate/High and High Risk classifications were cross-tabulated with SCID Drug Use Disorder diagnoses. In response to the PRO, almost one third of participants (29.4%) reported drug use during the 12 months pre-pregnancy awareness and 11.0% reported use post-pregnancy awareness; 7.0% met SCID diagnostic criteria for Drug Abuse, Drug Dependence, or both, primarily for marijuana use. Drug Use Disorder sensitivity and specificity rates for the PRO Moderate/High Risk classifications were 88.5% and 74.3%, respectively, and for High Risk only, 78.8% and 87.3%. The PRO yielded substantial self-reporting of drug use before and after pregnancy awareness with high sensitivity and specificity for detecting Drug Use Disorders. PRO results can inform decisions about appropriate clinical responses. © 2012 Wiley Periodicals, Inc.

Analysis of land cover change and its driving forces in a desert oasis landscape of southern Xinjiang, China

NASA Astrophysics Data System (ADS)

Amuti, T.; Luo, G.

2014-07-01

The combined effects of drought, warming and the changes in land cover have caused severe land degradation for several decades in the extremely arid desert oases of Southern Xinjiang, Northwest China. This study examined land cover changes during 1990-2008 to characterize and quantify the transformations in the typical oasis of Hotan. Land cover classifications of these images were performed based on the supervised classification scheme integrated with conventional vegetation and soil indexes. Change-detection techniques in remote sensing (RS) and a geographic information system (GIS) were applied to quantify temporal and spatial dynamics of land cover changes. The overall accuracies, Kappa coefficients, and average annual increase rate or decrease rate of land cover classes were calculated to assess classification results and changing rate of land cover. The analysis revealed that major trends of the land cover changes were the notable growth of the oasis and the reduction of the desert-oasis ecotone, which led to accelerated soil salinization and plant deterioration within the oasis. These changes were mainly attributed to the intensified human activities. The results indicated that the newly created agricultural land along the margins of the Hotan oasis could result in more potential areas of land degradation. If no effective measures are taken against the deterioration of the oasis environment, soil erosion caused by land cover change may proceed. The trend of desert moving further inward and the shrinking of the ecotone may lead to potential risks to the eco-environment of the Hotan oasis over the next decades.

Proton MRS detects Metabolic Changes in Hormone Sensitive and Resistant Human Prostate Cancer Model CWR22 and CWR22r

PubMed Central

Le, H. Carl; Lupu, Mihaela; Kotedia, Khushali; Rosen, Neal; Solit, David; Koutcher, Jason A.

2010-01-01

17-Allylamino, 17-Demethoxygeldanamycin (17-AAG), an effective inhibitor of the heat shock protein hsp90, preferentially inhibiting tumor hsp90 compared to hsp90 from normal cells (1), has shown promising results against several cancers, including hormone resistant prostate cancer. Levels of several oncogenic proteins critical to tumor growth and progression, such as AR (androgen receptor) and HER2/neu, were reduced 4 hours post 17-AAG treatment. Post treatment metabolic changes have also been observed in several tumor cell lines. In this study total choline (t-cho) distributions in hormone sensitive CWR22 and hormone resistant CWR22r prostate cancer xenograft tumors in mice were measured before, 4 hours and 48 hours after a single bolus 17-AAG treatment at 100 mg/kg using proton MRS. Our results show that tumor t-cho levels declined 4 hours after the treatment for CWR22 (P = 0.001) and 48 hours post treatment for CWR22r (P=0.003). Metabolic changes, in particular of t-cho intensity detected by 1H MRSI, are consistent with the observed immunohistochemistry changes, tumor growth inhibition for CWR22r (P=0.01 at 14 days post treatment) and a constant PSA level versus increasing PSA for control CWR22 (P=0.01). Metabolic changes in t-cho by proton MRSI can be used as an early biomarker of response for advanced stage prostate cancer in targeted therapy such as 17-AAG. PMID:19780165

Just-in-time classifiers for recurrent concepts.

PubMed

Alippi, Cesare; Boracchi, Giacomo; Roveri, Manuel

2013-04-01

Just-in-time (JIT) classifiers operate in evolving environments by classifying instances and reacting to concept drift. In stationary conditions, a JIT classifier improves its accuracy over time by exploiting additional supervised information coming from the field. In nonstationary conditions, however, the classifier reacts as soon as concept drift is detected; the current classification setup is discarded and a suitable one activated to keep the accuracy high. We present a novel generation of JIT classifiers able to deal with recurrent concept drift by means of a practical formalization of the concept representation and the definition of a set of operators working on such representations. The concept-drift detection activity, which is crucial in promptly reacting to changes exactly when needed, is advanced by considering change-detection tests monitoring both inputs and classes distributions.

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

NASA Astrophysics Data System (ADS)

Sa, Qila; Wang, Zhihui

2018-03-01

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

Detecting event-related changes in organizational networks using optimized neural network models.

PubMed

Li, Ze; Sun, Duoyong; Zhu, Renqi; Lin, Zihan

2017-01-01

Organizational external behavior changes are caused by the internal structure and interactions. External behaviors are also known as the behavioral events of an organization. Detecting event-related changes in organizational networks could efficiently be used to monitor the dynamics of organizational behaviors. Although many different methods have been used to detect changes in organizational networks, these methods usually ignore the correlation between the internal structure and external events. Event-related change detection considers the correlation and could be used for event recognition based on social network modeling and supervised classification. Detecting event-related changes could be effectively useful in providing early warnings and faster responses to both positive and negative organizational activities. In this study, event-related change in an organizational network was defined, and artificial neural network models were used to quantitatively determine whether and when a change occurred. To achieve a higher accuracy, Back Propagation Neural Networks (BPNNs) were optimized using Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO). We showed the feasibility of the proposed method by comparing its performance with that of other methods using two cases. The results suggested that the proposed method could identify organizational events based on a correlation between the organizational networks and events. The results also suggested that the proposed method not only has a higher precision but also has a better robustness than the previously used techniques.

Detecting event-related changes in organizational networks using optimized neural network models

PubMed Central

Sun, Duoyong; Zhu, Renqi; Lin, Zihan

2017-01-01

Organizational external behavior changes are caused by the internal structure and interactions. External behaviors are also known as the behavioral events of an organization. Detecting event-related changes in organizational networks could efficiently be used to monitor the dynamics of organizational behaviors. Although many different methods have been used to detect changes in organizational networks, these methods usually ignore the correlation between the internal structure and external events. Event-related change detection considers the correlation and could be used for event recognition based on social network modeling and supervised classification. Detecting event-related changes could be effectively useful in providing early warnings and faster responses to both positive and negative organizational activities. In this study, event-related change in an organizational network was defined, and artificial neural network models were used to quantitatively determine whether and when a change occurred. To achieve a higher accuracy, Back Propagation Neural Networks (BPNNs) were optimized using Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO). We showed the feasibility of the proposed method by comparing its performance with that of other methods using two cases. The results suggested that the proposed method could identify organizational events based on a correlation between the organizational networks and events. The results also suggested that the proposed method not only has a higher precision but also has a better robustness than the previously used techniques. PMID:29190799

Environmental mapping and monitoring of Iceland by remote sensing (EMMIRS)

NASA Astrophysics Data System (ADS)

Pedersen, Gro B. M.; Vilmundardóttir, Olga K.; Falco, Nicola; Sigurmundsson, Friðþór S.; Rustowicz, Rose; Belart, Joaquin M.-C.; Gísladóttir, Gudrun; Benediktsson, Jón A.

2016-04-01

Iceland is exposed to rapid and dynamic landscape changes caused by natural processes and man-made activities, which impact and challenge the country. Fast and reliable mapping and monitoring techniques are needed on a big spatial scale. However, currently there is lack of operational advanced information processing techniques, which are needed for end-users to incorporate remote sensing (RS) data from multiple data sources. Hence, the full potential of the recent RS data explosion is not being fully exploited. The project Environmental Mapping and Monitoring of Iceland by Remote Sensing (EMMIRS) bridges the gap between advanced information processing capabilities and end-user mapping of the Icelandic environment. This is done by a multidisciplinary assessment of two selected remote sensing super sites, Hekla and Öræfajökull, which encompass many of the rapid natural and man-made landscape changes that Iceland is exposed to. An open-access benchmark repository of the two remote sensing supersites is under construction, providing high-resolution LIDAR topography and hyperspectral data for land-cover and landform classification. Furthermore, a multi-temporal and multi-source archive stretching back to 1945 allows a decadal evaluation of landscape and ecological changes for the two remote sensing super sites by the development of automated change detection techniques. The development of innovative pattern recognition and machine learning-based approaches to image classification and change detection is one of the main tasks of the EMMIRS project, aiming to extract and compute earth observation variables as automatically as possible. Ground reference data collected through a field campaign will be used to validate the implemented methods, which outputs are then inferred with geological and vegetation models. Here, preliminary results of an automatic land-cover classification based on hyperspectral image analysis are reported. Furthermore, the EMMIRS project investigates the complex landscape dynamics between geological and ecological processes. This is done through cross-correlation of mapping results and implementation of modelling techniques that simulate geological and ecological processes in order to extrapolate the landscape evolution

The Feasibility Evaluation of Land Use Change Detection Using GAOFEN-3 Data

NASA Astrophysics Data System (ADS)

Huang, G.; Sun, Y.; Zhao, Z.

2018-04-01

GaoFen-3 (GF-3) satellite, is the first C band and multi-polarimetric Synthetic Aperture Radar (SAR) satellite in China. In order to explore the feasibility of GF-3 satellite in remote sensing interpretation and land-use remote sensing change detection, taking Guangzhou, China as a study area, the full polarimetric image of GF-3 satellite with 8 m resolution of two temporal as the data source. Firstly, the image is pre-processed by orthorectification, image registration and mosaic, and the land-use remote sensing digital orthophoto map (DOM) in 2017 is made according to the each county. Then the classification analysis and judgment of ground objects on the image are carried out by means of ArcGIS combining with the auxiliary data and using artificial visual interpretation, to determine the area of changes and the category of change objects. According to the unified change information extraction principle to extract change areas. Finally, the change detection results are compared with 3 m resolution TerraSAR-X data and 2 m resolution multi-spectral image, and the accuracy is evaluated. Experimental results show that the accuracy of the GF-3 data is over 75 % in detecting the change of ground objects, and the detection capability of new filling soil is better than that of TerraSAR-X data, verify the detection and monitoring capability of GF-3 data to the change information extraction, also, it shows that GF-3 can provide effective data support for the remote sensing detection of land resources.

Using Land Surface Phenology to Detect Land Use Change in the Northern Great Plains

NASA Astrophysics Data System (ADS)

Nguyen, L. H.; Henebry, G. M.

2017-12-01

The Northern Great Plains of the US have been undergoing many types of land cover / land use change over the past two decades, including expansion of irrigation, conversion of grassland to cropland, biofuels production, urbanization, and fossil fuel mining. Much of the literature on these changes has relied on post-classification change detection based on a limited number of observations per year. Here we demonstrate an approach to characterize land dynamics through land surface phenology (LSP) by synergistic use of image time series at two scales. Our study areas include regions of interest (ROIs) across the Northern Great Plains located within Landsat path overlap zones to boost the number of valid observations (free of clouds or snow) each year. We first compute accumulated growing degree-days (AGDD) from MODIS 8-day composites of land surface temperature (MOD11A2 and MYD11A2). Using Landsat Collection 1 surface reflectance-derived vegetation indices (NDVI, EVI), we then fit at each pixel a downward convex quadratic model linking the vegetation index to each year's progression of AGDD. This quadratic equation exhibits linearity in a mathematical sense; thus, the fitted models can be linearly mixed and unmixed using a set of LSP endmembers (defined by the fitted parameter coefficients of the quadratic model) that represent "pure" land cover types with distinct seasonal patterns found within the region, such as winter wheat, spring wheat, maize, soybean, sunflower, hay/pasture/grassland, developed/built-up, among others. Information about land cover corresponding to each endmember are provided by the NLCD (National Land Cover Dataset) and CDL (Cropland Data Layer). We use linear unmixing to estimate the likely proportion of each LSP endmember within particular areas stratified by latitude. By tracking the proportions over the 2001-2011 period, we can quantify various types of land transitions in the Northern Great Plains.

Raman spectroscopic study of keratin 8 knockdown oral squamous cell carcinoma derived cells

NASA Astrophysics Data System (ADS)

Singh, S. P.; Alam, Hunain; Dmello, Crismita; Vaidya, Milind M.; Krishna, C. Murali

2012-03-01

Keratins are one of most widely used markers for oral cancers. Keratin 8 and 18 are expressed in simple epithelia and perform both mechanical and regulatory functions. Their expression are not seen in normal oral tissues but are often expressed in oral squamous cell carcinoma. Aberrant expression of keratins 8 and 18 is most common change in human oral cancer. Optical-spectroscopic methods are sensitive to biochemical changes and being projected as novel diagnostic tools for cancer diagnosis. Aim of this study was to evaluate potentials of Raman spectroscopy in detecting minor changes associated with differential level of keratin expression in tongue-cancer-derived AW13516 cells. Knockdown clones for K8 were generated and synchronized by growing under serum-free conditions. Cell pellets of three independent experiments in duplicate were used for recording Raman spectra with fiberoptic-probe coupled HE-785 Raman-instrument. A total of 123 and 96 spectra from knockdown clones and vector controls respectively in 1200-1800 cm-1 region were successfully utilized for classification using LDA. Two separate clusters with classification-efficiency of ~95% were obtained. Leave-one-out cross-validation yielded ~63% efficiency. Findings of the study demonstrate the potentials of Raman spectroscopy in detecting even subtle changes such as variations in keratin expression levels. Future studies towards identifying Raman signals from keratin in oral cells can help in precise cancer diagnosis.

1H NMR spectroscopic analysis detects metabolic disturbances in rat urine on acute exposure to heavy metal tungsten alloy based metals salt.

PubMed

Tyagi, Ritu; Rana, Poonam; Gupta, Mamta; Bhatnagar, Deepak; Srivastava, Shatakshi; Roy, Raja; Khushu, Subash

2014-03-25

Heavy metal tungsten alloys (HMTAs) have been found to be safer alternatives for making military munitions. Recently, some studies demonstrating the toxic potential of HMTAs have raised concern over the safety issues, and further propose that HMTAs exposure may lead to physiological disturbances as well. To look for the systemic effect of acute toxicity of HMTA based metals salt, (1)H nuclear magnetic resonance ((1)H NMR) spectroscopic profiling of rat urine was carried out. Male Sprague Dawley rats were administered (intraperitoneal) low and high dose of mixture of HMTA based metals salt and NMR spectroscopy was carried out in urine samples collected at 8, 24, 72 and 120 h post dosing (p.d.). Serum biochemical parameters and liver histopathology were also conducted. The (1)H NMR spectra were analysed using multivariate analysis techniques to show the time- and dose-dependent biochemical variations in post HMTA based metals salt exposure. Urine metabolomic analysis showed changes associated with energy metabolism, amino acids, N-methyl nicotinamide, membrane and gut flora metabolites. Multivariate analysis showed maximum variation with best classification of control and treated groups at 24h p.d. At the end of the study, for the low dose group most of the changes at metabolite level reverted to control except for the energy metabolites; whereas, in the high dose group some of the changes still persisted. The observations were well correlated with histopathological and serum biochemical parameters. Further, metabolic pathway analysis clarified that amongst all the metabolic pathways analysed, tricarboxylic acid cycle was most affected at all the time points indicating a switchover in energy metabolism from aerobic to anaerobic. These results suggest that exposure of rats to acute doses of HMTA based metals salt disrupts physiological metabolism with moderate injury to the liver, which might indirectly result from heavy metals induced oxidative stress. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Applicability Assessment of Uavsar Data in Wetland Monitoring: a Case Study of Louisiana Wetland

NASA Astrophysics Data System (ADS)

Zhao, J.; Niu, Y.; Lu, Z.; Yang, J.; Li, P.; Liu, W.

2018-04-01

Wetlands are highly productive and support a wide variety of ecosystem goods and services. Monitoring wetland is essential and potential. Because of the repeat-pass nature of satellite orbit and airborne, time-series of remote sensing data can be obtained to monitor wetland. UAVSAR is a NASA L-band synthetic aperture radar (SAR) sensor compact pod-mounted polarimetric instrument for interferometric repeat-track observations. Moreover, UAVSAR images can accurately map crustal deformations associated with natural hazards, such as volcanoes and earthquakes. And its polarization agility facilitates terrain and land-use classification and change detection. In this paper, the multi-temporal UAVSAR data are applied for monitoring the wetland change. Using the multi-temporal polarimetric SAR (PolSAR) data, the change detection maps are obtained by unsupervised and supervised method. And the coherence is extracted from the interfometric SAR (InSAR) data to verify the accuracy of change detection map. The experimental results show that the multi-temporal UAVSAR data is fit for wetland monitor.

Mass detection, localization and estimation for wind turbine blades based on statistical pattern recognition

NASA Astrophysics Data System (ADS)

Colone, L.; Hovgaard, M. K.; Glavind, L.; Brincker, R.

2018-07-01

A method for mass change detection on wind turbine blades using natural frequencies is presented. The approach is based on two statistical tests. The first test decides if there is a significant mass change and the second test is a statistical group classification based on Linear Discriminant Analysis. The frequencies are identified by means of Operational Modal Analysis using natural excitation. Based on the assumption of Gaussianity of the frequencies, a multi-class statistical model is developed by combining finite element model sensitivities in 10 classes of change location on the blade, the smallest area being 1/5 of the span. The method is experimentally validated for a full scale wind turbine blade in a test setup and loaded by natural wind. Mass change from natural causes was imitated with sand bags and the algorithm was observed to perform well with an experimental detection rate of 1, localization rate of 0.88 and mass estimation rate of 0.72.

Automated Sunspot Detection and Classification Using SOHO/MDI Imagery

DTIC Science & Technology

2015-03-01

atmosphere cause the refractive index to vary [1], thus causing distortion in the image as the light rays forming the image take different optical paths...available from the National Oceanic and Atmospheric Administration’s (NOAA) Solar Region Summaries (SRS) in that it does not change with the biases of...41 IV. Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.1 Database

Rapid Disaster Analysis based on Remote Sensing: A Case Study about the Tohoku Tsunami Disaster 2011

NASA Astrophysics Data System (ADS)

Yang, C. H.; Soergel, U.; Lanaras, Ch.; Baltsavias, E.; Cho, K.; Remondino, F.; Wakabayashi, H.

2014-09-01

In this study, we present first results of RAPIDMAP, a project funded by European Union in a framework aiming to foster the cooperation of European countries with Japan in R&D. The main objective of RAPIDMAP is to construct a Decision Support System (DSS) based on remote sensing data and WebGIS technologies, where users can easily access real-time information assisting with disaster analysis. In this paper, we present a case study of the Tohoku Tsunami Disaster 2011. We address two approaches namely change detection based on SAR data and co-registration of optical and SAR satellite images. With respect to SAR data, our efforts are subdivided into three parts: (1) initial coarse change detection for entire area, (2) flood area detection, and (3) linearfeature change detection. The investigations are based on pre- and post-event TerraSAR-X images. In (1), two pre- and post-event TerraSAR-X images are accurately co-registered and radiometrically calibrated. Data are fused in a false-color image that provides a quick and rough overview of potential changes, which is useful for initial decision making and identifying areas worthwhile to be analysed further in more depth. However, a bunch of inevitable false alarms appear within the scene caused by speckle, temporal decorrelation, co-registration inaccuracy and so on. In (2), the post-event TerraSAR-X data are used to extract the flood area by using thresholding and morphological approaches. The validated result indicates that using SAR data combining with suitable morphological approaches is a quick and effective way to detect flood area. Except for usage of SAR data, the false-color image composed of optical images are also used to detect flood area for further exploration in this part. In (3), Curvelet filtering is applied in the difference image of pre- and post-event TerraSAR-X images not only to suppress false alarms of irregular-features, but also to enhance the change signals of linear-features (e.g. buildings) in settlements. Afterwards, thresholding is exploited to extract the linear-feature changes. In rapid mapping of disasters various sensors are often employed, including optical and SAR, since they provide complementary information. Such data needs to be analyzed in an integrated fashion and the results from each dataset should be integrated in a GIS with a common coordinate reference system. Thus, if no orthoimages can be generated, the images should be co-registered employing matching of common features. We present results of co-registration between optical (FORMOSAT-2) and TerraSAR-X images based on different matching methods, and also techniques for detecting and eliminating matching errors.

Rapid Landslide Mapping by Means of Post-Event Polarimetric SAR Imagery

NASA Astrophysics Data System (ADS)

Plank, Simon; Martinis, Sandro; Twele, Andre

2016-08-01

Rapid mapping of landslides, quickly providing information about the extent of the affected area and type and grade of damage, is crucial to enable fast crisis response. Reviewing the literature shows that most synthetic aperture radar (SAR) data-based landslide mapping procedures use change detection techniques. However, the required very high resolution (VHR) pre-event SAR imagery, acquired shortly before the landslide event, is commonly not available. Due to limitations in onboard disk space and downlink transmission rates modern VHR SAR missions do not systematically cover the entire world. We present a fast and robust procedure for mapping of landslides, based on change detection between freely available and systematically acquired pre-event optical and post-event polarimetric SAR data.

Hierarchical classification of land use types using multiple vegetation indices to measure the effects of urbanization.

PubMed

Shishir, Sharmin; Tsuyuzaki, Shiro

2018-05-11

Detecting fine-scale spatiotemporal land use changes is a prerequisite for understanding and predicting the effects of urbanization and its related human impacts on the ecosystem. Land use changes are frequently examined using vegetation indices (VIs), although the validation of these indices has not been conducted at a high resolution. Therefore, a hierarchical classification was constructed to obtain accurate land use types at a fine scale. The characteristics of four popular VIs were investigated prior to examining the hierarchical classification by using Purbachal New Town, Bangladesh, which exhibits ongoing urbanization. These four VIs are the normalized difference VI (NDVI), green-red VI (GRVI), enhanced VI (EVI), and two-band EVI (EVI2). The reflectance data were obtained by the IKONOS (0.8-m resolution) and WorldView-2 sensor (0.5-m resolution) in 2001 and 2015, respectively. The hierarchical classification of land use types was constructed using a decision tree (DT) utilizing all four of the examined VIs. The accuracy of the classification was evaluated using ground truth data with multiple comparisons and kappa (κ) coefficients. The DT showed overall accuracies of 96.1 and 97.8% in 2001 and 2015, respectively, while the accuracies of the VIs were less than 91.2%. These results indicate that each VI exhibits unique advantages. In addition, the DT was the best classifier of land use types, particularly for native ecosystems represented by Shorea forests and homestead vegetation, at the fine scale. Since the conservation of these native ecosystems is of prime importance, DTs based on hierarchical classifications should be used more widely.

Bayesian cloud detection for MERIS, AATSR, and their combination

NASA Astrophysics Data System (ADS)

Hollstein, A.; Fischer, J.; Carbajal Henken, C.; Preusker, R.

2015-04-01

A broad range of different of Bayesian cloud detection schemes is applied to measurements from the Medium Resolution Imaging Spectrometer (MERIS), the Advanced Along-Track Scanning Radiometer (AATSR), and their combination. The cloud detection schemes were designed to be numerically efficient and suited for the processing of large numbers of data. Results from the classical and naive approach to Bayesian cloud masking are discussed for MERIS and AATSR as well as for their combination. A sensitivity study on the resolution of multidimensional histograms, which were post-processed by Gaussian smoothing, shows how theoretically insufficient numbers of truth data can be used to set up accurate classical Bayesian cloud masks. Sets of exploited features from single and derived channels are numerically optimized and results for naive and classical Bayesian cloud masks are presented. The application of the Bayesian approach is discussed in terms of reproducing existing algorithms, enhancing existing algorithms, increasing the robustness of existing algorithms, and on setting up new classification schemes based on manually classified scenes.

Sound and speech detection and classification in a Health Smart Home.

PubMed

Fleury, A; Noury, N; Vacher, M; Glasson, H; Seri, J F

2008-01-01

Improvements in medicine increase life expectancy in the world and create a new bottleneck at the entrance of specialized and equipped institutions. To allow elderly people to stay at home, researchers work on ways to monitor them in their own environment, with non-invasive sensors. To meet this goal, smart homes, equipped with lots of sensors, deliver information on the activities of the person and can help detect distress situations. In this paper, we present a global speech and sound recognition system that can be set-up in a flat. We placed eight microphones in the Health Smart Home of Grenoble (a real living flat of 47m(2)) and we automatically analyze and sort out the different sounds recorded in the flat and the speech uttered (to detect normal or distress french sentences). We introduce the methods for the sound and speech recognition, the post-processing of the data and finally the experimental results obtained in real conditions in the flat.

Multivariate classification of the infrared spectra of cell and tissue samples

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

Haaland, D.M.; Jones, H.D.; Thomas, E.V.

1997-03-01

Infrared microspectroscopy of biopsied canine lymph cells and tissue was performed to investigate the possibility of using IR spectra coupled with multivariate classification methods to classify the samples as normal, hyperplastic, or neoplastic (malignant). IR spectra were obtained in transmission mode through BaF{sub 2} windows and in reflection mode from samples prepared on gold-coated microscope slides. Cytology and histopathology samples were prepared by a variety of methods to identify the optimal methods of sample preparation. Cytospinning procedures that yielded a monolayer of cells on the BaF{sub 2} windows produced a limited set of IR transmission spectra. These transmission spectra weremore » converted to absorbance and formed the basis for a classification rule that yielded 100{percent} correct classification in a cross-validated context. Classifications of normal, hyperplastic, and neoplastic cell sample spectra were achieved by using both partial least-squares (PLS) and principal component regression (PCR) classification methods. Linear discriminant analysis applied to principal components obtained from the spectral data yielded a small number of misclassifications. PLS weight loading vectors yield valuable qualitative insight into the molecular changes that are responsible for the success of the infrared classification. These successful classification results show promise for assisting pathologists in the diagnosis of cell types and offer future potential for {ital in vivo} IR detection of some types of cancer. {copyright} {ital 1997} {ital Society for Applied Spectroscopy}« less

GPU based cloud system for high-performance arrhythmia detection with parallel k-NN algorithm.

PubMed

Tae Joon Jun; Hyun Ji Park; Hyuk Yoo; Young-Hak Kim; Daeyoung Kim

2016-08-01

In this paper, we propose an GPU based Cloud system for high-performance arrhythmia detection. Pan-Tompkins algorithm is used for QRS detection and we optimized beat classification algorithm with K-Nearest Neighbor (K-NN). To support high performance beat classification on the system, we parallelized beat classification algorithm with CUDA to execute the algorithm on virtualized GPU devices on the Cloud system. MIT-BIH Arrhythmia database is used for validation of the algorithm. The system achieved about 93.5% of detection rate which is comparable to previous researches while our algorithm shows 2.5 times faster execution time compared to CPU only detection algorithm.

On the classification of normalized natural frequencies for damage detection in cantilever beam

NASA Astrophysics Data System (ADS)

Dahak, Mustapha; Touat, Noureddine; Benseddiq, Noureddine

2017-08-01

The presence of a damage on a beam causes changes in the physical properties, which introduce flexibility, and reduce the natural frequencies of the beam. Based on this, a new method is proposed to locate the damage zone in a cantilever beam. In this paper, the cantilever beam is discretized into a number of zones, where each zone has a specific classification of the first four normalized natural frequencies. The damaged zone is distinguished by only the classification of the normalized frequencies of the structure. In the case when the damage is symmetric to the vibration node, we use the unchanged natural frequency as a second information to obtain a more accurate location. The effectiveness of the proposed method is shown by a numerical simulation with ANSYS software and experimental investigation of a cantilever beam with different damage.

Detection of Cardiac Abnormalities from Multilead ECG using Multiscale Phase Alternation Features.

PubMed

Tripathy, R K; Dandapat, S

2016-06-01

The cardiac activities such as the depolarization and the relaxation of atria and ventricles are observed in electrocardiogram (ECG). The changes in the morphological features of ECG are the symptoms of particular heart pathology. It is a cumbersome task for medical experts to visually identify any subtle changes in the morphological features during 24 hours of ECG recording. Therefore, the automated analysis of ECG signal is a need for accurate detection of cardiac abnormalities. In this paper, a novel method for automated detection of cardiac abnormalities from multilead ECG is proposed. The method uses multiscale phase alternation (PA) features of multilead ECG and two classifiers, k-nearest neighbor (KNN) and fuzzy KNN for classification of bundle branch block (BBB), myocardial infarction (MI), heart muscle defect (HMD) and healthy control (HC). The dual tree complex wavelet transform (DTCWT) is used to decompose the ECG signal of each lead into complex wavelet coefficients at different scales. The phase of the complex wavelet coefficients is computed and the PA values at each wavelet scale are used as features for detection and classification of cardiac abnormalities. A publicly available multilead ECG database (PTB database) is used for testing of the proposed method. The experimental results show that, the proposed multiscale PA features and the fuzzy KNN classifier have better performance for detection of cardiac abnormalities with sensitivity values of 78.12 %, 80.90 % and 94.31 % for BBB, HMD and MI classes. The sensitivity value of proposed method for MI class is compared with the state-of-art techniques from multilead ECG.

Abnormal global and local event detection in compressive sensing domain

NASA Astrophysics Data System (ADS)

Wang, Tian; Qiao, Meina; Chen, Jie; Wang, Chuanyun; Zhang, Wenjia; Snoussi, Hichem

2018-05-01

Abnormal event detection, also known as anomaly detection, is one challenging task in security video surveillance. It is important to develop effective and robust movement representation models for global and local abnormal event detection to fight against factors such as occlusion and illumination change. In this paper, a new algorithm is proposed. It can locate the abnormal events on one frame, and detect the global abnormal frame. The proposed algorithm employs a sparse measurement matrix designed to represent the movement feature based on optical flow efficiently. Then, the abnormal detection mission is constructed as a one-class classification task via merely learning from the training normal samples. Experiments demonstrate that our algorithm performs well on the benchmark abnormal detection datasets against state-of-the-art methods.

Affective assessment of computer users based on processing the pupil diameter signal.

PubMed

Ren, Peng; Barreto, Armando; Gao, Ying; Adjouadi, Malek

2011-01-01

Detecting affective changes of computer users is a current challenge in human-computer interaction which is being addressed with the help of biomedical engineering concepts. This article presents a new approach to recognize the affective state ("relaxation" vs. "stress") of a computer user from analysis of his/her pupil diameter variations caused by sympathetic activation. Wavelet denoising and Kalman filtering methods are first used to remove abrupt changes in the raw Pupil Diameter (PD) signal. Then three features are extracted from the preprocessed PD signal for the affective state classification. Finally, a random tree classifier is implemented, achieving an accuracy of 86.78%. In these experiments the Eye Blink Frequency (EBF), is also recorded and used for affective state classification, but the results show that the PD is a more promising physiological signal for affective assessment.

Audit of practice in sudden unexpected death in epilepsy (SUDEP) post mortems and neuropathological findings

PubMed Central

Michalak, Zuzanna; Wright, Gabriella; Dawson, Timothy; Hilton, David; Joshi, Abhijit; Diehl, Beate; Koepp, Matthias; Lhatoo, Samden; Sander, Josemir W.; Sisodiya, Sanjay M.

2015-01-01

Aims Sudden unexpected death in epilepsy (SUDEP) is one of the leading causes of death in people with epilepsy. For classification of definite SUDEP, a post mortem (PM), including anatomical and toxicological examination, is mandatory to exclude other causes of death. We audited PM practice as well as the value of brain examination in SUDEP. Methods We reviewed 145 PM reports in SUDEP cases from four UK neuropathology centres. Data were extracted for clinical epilepsy details, circumstances of death and neuropathological findings. Results Macroscopic brain abnormalities were identified in 52% of cases. Mild brain swelling was present in 28%, and microscopic pathologies relevant to cause or effect of seizures were seen in 89%. Examination based on whole fixed brains (76.6% of all PMs), and systematic regional sampling was associated with higher detection rates of underlying pathology (P < 0.01). Information was more frequently recorded regarding circumstances of death and body position/location than clinical epilepsy history and investigations. Conclusion Our findings support the contribution of examination of the whole fixed brain in SUDEP, with high rates of detection of relevant pathology. Availability of full clinical epilepsy‐related information at the time of PM could potentially further improve detection through targeted tissue sampling. Apart from confirmation of SUDEP, complete neuropathological examination contributes to evaluation of risk factors as well as helping to direct future research into underlying causes. PMID:26300477

75 FR 10529 - Mail Classification Change

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-08

... POSTAL REGULATORY COMMISSION [Docket Nos. MC2010-19; Order No. 415] Mail Classification Change...-filed Postal Service request to make a minor modification to the Mail Classification Schedule. The.... concerning a change in classification which reflects a change in terminology from Bulk Mailing Center (BMC...

Tracking the Effect of Algal Mats on Coral Bleaching Using Remote Sensing

NASA Astrophysics Data System (ADS)

El-Askary, H. M.; Johnson, S. H.; Idris, N.; Qurban, M. A. B.

2014-12-01

Benthic habitats rely on relatively stable environmental conditions for survival. The introduction of algal mats into an ecosystem can have a notable effect on the livelihood of organisms such as coral reefs by causing changes in the biogeochemistry of the surrounding water. Increasing levels of acidity and new competition for sunlight caused by congregations of cyanobacteria essentially starve coral reefs of natural resources. These changes are particularly prevalent in waters near quickly developing population centers, such as the ecologically diverse Arabian Gulf. While ground-truthing studies to determine the extensiveness of coral death proves useful on a microcosmic level, new ventures in remote sensing research allow scientists to utilize satellite data to track these changes on a broader scale. Satellite images acquired from Landsat 5, 1987, Landsat 7, 2000, and Landsat 8, 2013 along with higher resolution IKONOS data are digitally analyzed in order to create spectral libraries for relevant benthic types, which in turn can be used to perform supervised classifications and change detection analyses over a larger area. The supervised classifications performed over the three scenes show five significant marine-related classes, namely coral, mangroves, macro-algae, and seagrass, in different degrees of abundance, yet here we focus only on the algal mats impact on corals bleaching. The change detection analysis is introduced to study see the degree of algal mats impact on coral bleaching over the course of time with possible connection to the local meteorology and current climate scenarios.

Imaging of juvenile idiopathic arthritis. Part I: Clinical classifications and radiographs

PubMed Central

Matuszewska, Genowefa; Gietka, Piotr; Płaza, Mateusz; Walentowska-Janowicz, Marta

2016-01-01

Juvenile idiopathic arthritis is the most common autoimmune systemic disease of the connective tissue affecting individuals at the developmental age. Radiography is the primary modality employed in the diagnostic imaging in order to identify changes typical of this disease entity and rule out other bone-related pathologies, such as neoplasms, posttraumatic changes, developmental defects and other forms of arthritis. The standard procedure involves the performance of comparative joint radiographs in two planes. Radiographic changes in juvenile idiopathic arthritis are detected in later stages of the disease. Bone structures are assessed in the first place. Radiographs can also indirectly indicate the presence of soft tissue inflammation (i.e. in joint cavities, sheaths and bursae) based on swelling and increased density of the soft tissue as well as dislocation of fat folds. Signs of articular cartilage defects are also seen in radiographs indirectly – based on joint space width changes. The first part of the publication presents the classification of juvenile idiopathic arthritis and discusses its radiographic images. The authors list the affected joints as well as explain the spectrum and specificity of radiographic signs resulting from inflammatory changes overlapping with those caused by the maturation of the skeletal system. Moreover, certain dilemmas associated with the monitoring of the disease are reviewed. The second part of the publication will explain issues associated with ultrasonography and magnetic resonance imaging, which are more and more commonly applied in juvenile idiopathic arthritis for early detection of pathological features as well as the disease complications. PMID:27679726

Image-based fall detection and classification of a user with a walking support system

NASA Astrophysics Data System (ADS)

Taghvaei, Sajjad; Kosuge, Kazuhiro

2017-10-01

The classification of visual human action is important in the development of systems that interact with humans. This study investigates an image-based classification of the human state while using a walking support system to improve the safety and dependability of these systems.We categorize the possible human behavior while utilizing a walker robot into eight states (i.e., sitting, standing, walking, and five falling types), and propose two different methods, namely, normal distribution and hidden Markov models (HMMs), to detect and recognize these states. The visual feature for the state classification is the centroid position of the upper body, which is extracted from the user's depth images. The first method shows that the centroid position follows a normal distribution while walking, which can be adopted to detect any non-walking state. The second method implements HMMs to detect and recognize these states. We then measure and compare the performance of both methods. The classification results are employed to control the motion of a passive-type walker (called "RT Walker") by activating its brakes in non-walking states. Thus, the system can be used for sit/stand support and fall prevention. The experiments are performed with four subjects, including an experienced physiotherapist. Results show that the algorithm can be adapted to the new user's motion pattern within 40 s, with a fall detection rate of 96.25% and state classification rate of 81.0%. The proposed method can be implemented to other abnormality detection/classification applications that employ depth image-sensing devices.

A Pathogenetic Classification of Diabetic Macular Edema.

PubMed

Parodi Battaglia, Maurizio; Iacono, Pierluigi; Cascavilla, Marialucia; Zucchiatti, Ilaria; Bandello, Francesco

2018-04-11

The aim of this study was to define a new pathogenetic classification of diabetic macular edema (DME) and to present the results of its application in common clinical practice. One hundred and seventy-seven consecutive patients with center-involving DME, central retinal thickness (CRT) ≥250 µm, were prospectively enrolled. A complete ophthalmological examination included best-corrected visual acuity (BCVA) assessment, fundus photography, and spectral-domain optical coherence tomography (OCT). The DME classification was broken down into 4 categories, combining the presence of retinal thickening with the presence/absence of visible vascular dilations and OCT-detectable macular traction. The OCT parameters included were as follows: CRT, subretinal fluid, intraretinal cysts, and hyper- reflective foci (HF). Four subtypes of DME were identified: vasogenic (131 eyes, DME with vascular dilation), nonvasogenic (46 eyes, DME without vascular dilation), tractional (11 eyes), and mixed DME (13 eyes). Vasogenic DME was the pattern mainly represented in each subclass of CRT (< 300, 300-400, and > 400 µm), with tractional DME observed especially with CRT > 400 µm. Internal and external cysts and a greater presence of hard exudates were predominantly found in vasogenic DME, whereas HF was equally distributed in the 4 DME subgroups. The study offers a new pathogenetic classification able to detect significant differences among DME subtypes. A tailored therapeutic approach could take into consideration specific changes associated with the different DME subtypes. © 2018 S. Karger AG, Basel.

Optoacoustic Imaging Detects Hormone-Related Physiological Changes of Breast Parenchyma.

PubMed

Abeyakoon, Oshaani; Morscher, Stefan; Dalhaus, Nina; Ford, Steven J; Mendichovszky, Iosif A; Manavaki, Roido; Wallis, Matthew; Moyle, Penelope; Woitek, Ramona; Patterson, Andrew; Torheim, Turid; Joseph, James; Gonzalez, Isabel Quiros; Bohndiek, Sarah; Gilbert, Fiona J

2018-06-07

 Optoacoustic imaging with ultrasound (OPUS) can assess in-vivo perfusion/oxygenation through surrogate measures of oxy, deoxy and total hemoglobin content in tissues. The primary aim of our study was to evaluate the ability of OPUS to detect physiological changes in the breast during the menstrual cycle and to determine qualitative/quantitative metrics of normal parenchymal tissue in pre-/post-menopausal women. The secondary aim was to assess the technique's repeatability.  We performed a prospective ethically approved study in volunteers using OPUS (700, 800 and 850 nm wavelengths) in the proliferative/follicular and secretory phase of the menstrual cycle. Regions of interest (ROIs) were drawn on the most superficial region of fibroglandular tissue and same-day intra-observer repeatability was assessed. We used t-tests to interrogate differences in the OPUS measurements due to hormonal changes and interclass correlation coefficients/Bland-Altman plots to evaluate the repeatability of mean ROI signal intensities.  22 pre-menopausal and 8 post-menopausal volunteers were recruited. 21 participants underwent repeatability examinations. OPUS intensity values were significantly higher (p < 0.0001) at all excitation wavelengths in the secretory compared to the proliferative/follicular phase. Post-menopausal volunteers showed similar optoacoustic values to the proliferative/follicular phase of pre-menopausal volunteers. The repeatability of the technique was comparable to other handheld ultrasound modalities.  OPUS detects changes in perfusion/vascularity related to the menstrual cycle and menopausal status of breast parenchyma. © Georg Thieme Verlag KG Stuttgart · New York.

Label-free image-based detection of drug resistance with optofluidic time-stretch microscopy (Conference Presentation)

NASA Astrophysics Data System (ADS)

Kobayashi, Hirofumi; Lei, Cheng; Mao, Ailin; Jiang, Yiyue; Guo, Baoshan; Ozeki, Yasuyuki; Goda, Keisuke

2017-02-01

Acquired drug resistance is a fundamental predicament in cancer therapy. Early detection of drug-resistant cancer cells during or after treatment is expected to benefit patients from unnecessary drug administration and thus play a significant role in the development of a therapeutic strategy. However, the development of an effective method of detecting drug-resistant cancer cells is still in its infancy due to their complex mechanism in drug resistance. To address this problem, we propose and experimentally demonstrate label-free image-based drug resistance detection with optofluidic time-stretch microscopy using leukemia cells (K562 and K562/ADM). By adding adriamycin (ADM) to both K562 and K562/ADM (ADM-resistant K562 cells) cells, both types of cells express unique morphological changes, which are subsequently captured by an optofluidic time-stretch microscope. These unique morphological changes are extracted as image features and are subjected to supervised machine learning for cell classification. We hereby have successfully differentiated K562 and K562/ADM solely with label-free images, which suggests that our technique is capable of detecting drug-resistant cancer cells. Our optofluidic time-stretch microscope consists of a time-stretch microscope with a high spatial resolution of 780 nm at a 1D frame rate of 75 MHz and a microfluidic device that focuses and orders cells. We compare various machine learning algorithms as well as various concentrations of ADM for cell classification. Owing to its unprecedented versatility of using label-free image and its independency from specific molecules, our technique holds great promise for detecting drug resistance of cancer cells for which its underlying mechanism is still unknown or chemical probes are still unavailable.

A Global Classification of Contemporary Fire Regimes

NASA Astrophysics Data System (ADS)

Norman, S. P.; Kumar, J.; Hargrove, W. W.; Hoffman, F. M.

2014-12-01

Fire regimes provide a sensitive indicator of changes in climate and human use as the concept includes fire extent, season, frequency, and intensity. Fires that occur outside the distribution of one or more aspects of a fire regime may affect ecosystem resilience. However, global scale data related to these varied aspects of fire regimes are highly inconsistent due to incomplete or inconsistent reporting. In this study, we derive a globally applicable approach to characterizing similar fire regimes using long geophysical time series, namely MODIS hotspots since 2000. K-means non-hierarchical clustering was used to generate empirically based groups that minimized within-cluster variability. Satellite-based fire detections are known to have shortcomings, including under-detection from obscuring smoke, clouds or dense canopy cover and rapid spread rates, as often occurs with flashy fuels or during extreme weather. Such regions are free from preconceptions, and the empirical, data-mining approach used on this relatively uniform data source allows the region structures to emerge from the data themselves. Comparing such an empirical classification to expectations from climate, phenology, land use or development-based models can help us interpret the similarities and differences among places and how they provide different indicators of changes of concern. Classifications can help identify where large infrequent mega-fires are likely to occur ahead of time such as in the boreal forest and portions of the Interior US West, and where fire reports are incomplete such as in less industrial countries.

Remote sensing based detection of forested wetlands: An evaluation of LiDAR, aerial imagery, and their data fusion

NASA Astrophysics Data System (ADS)

Suiter, Ashley Elizabeth

Multi-spectral imagery provides a robust and low-cost dataset for assessing wetland extent and quality over broad regions and is frequently used for wetland inventories. However in forested wetlands, hydrology is obscured by tree canopy making it difficult to detect with multi-spectral imagery alone. Because of this, classification of forested wetlands often includes greater errors than that of other wetlands types. Elevation and terrain derivatives have been shown to be useful for modelling wetland hydrology. But, few studies have addressed the use of LiDAR intensity data detecting hydrology in forested wetlands. Due the tendency of LiDAR signal to be attenuated by water, this research proposed the fusion of LiDAR intensity data with LiDAR elevation, terrain data, and aerial imagery, for the detection of forested wetland hydrology. We examined the utility of LiDAR intensity data and determined whether the fusion of Lidar derived data with multispectral imagery increased the accuracy of forested wetland classification compared with a classification performed with only multi-spectral image. Four classifications were performed: Classification A -- All Imagery, Classification B -- All LiDAR, Classification C -- LiDAR without Intensity, and Classification D -- Fusion of All Data. These classifications were performed using random forest and each resulted in a 3-foot resolution thematic raster of forested upland and forested wetland locations in Vermilion County, Illinois. The accuracies of these classifications were compared using Kappa Coefficient of Agreement. Importance statistics produced within the random forest classifier were evaluated in order to understand the contribution of individual datasets. Classification D, which used the fusion of LiDAR and multi-spectral imagery as input variables, had moderate to strong agreement between reference data and classification results. It was found that Classification A performed using all the LiDAR data and its derivatives (intensity, elevation, slope, aspect, curvatures, and Topographic Wetness Index) was the most accurate classification with Kappa: 78.04%, indicating moderate to strong agreement. However, Classification C, performed with LiDAR derivative without intensity data had less agreement than would be expected by chance, indicating that LiDAR contributed significantly to the accuracy of Classification B.

Detection and characterization of benthic filamentous algal stands (Cladophora sp.) on rocky substrata using a high-frequency echosounder

USGS Publications Warehouse

Depew, David C.; Stevens, Andrew W.; Smith, Ralph E.H.; Hecky, Robert E.

2009-01-01

A high-frequency echosounder was used to detect and characterize percent cover and stand height of the benthic filamentous green alga Cladophora sp. on rocky substratum of the Laurentian Great Lakes. Comparisons between in situ observations and estimates of the algal stand characteristics (percent cover, stand height) derived from the acoustic data show good agreement for algal stands that exceeded the height threshold for detection by acoustics (~7.5 cm). Backscatter intensity and volume scattering strength were unable to provide any predictive power for estimating algal biomass. A comparative analysis between the only current commercial software (EcoSAV™) and an alternate method using a graphical user interface (GUI) written in MATLAB® confirmed previous findings that EcoSAV functions poorly in conditions where the substrate is uneven and bottom depth changes rapidly. The GUI method uses a signal processing algorithm similar to that of EcoSAV but bases bottom depth classification and algal stand height classification on adjustable thresholds that can be visualized by a trained analyst. This study documents the successful characterization of nuisance quantities of filamentous algae on hard substrate using an acoustic system and demonstrates the potential to significantly increase the efficiency of collecting information on the distribution of nuisance macroalgae. This study also highlights the need for further development of more flexible classification algorithms that can be used in a variety of aquatic ecosystems.

Ultrasonic sensor based defect detection and characterisation of ceramics.

PubMed

Kesharaju, Manasa; Nagarajah, Romesh; Zhang, Tonzhua; Crouch, Ian

2014-01-01

Ceramic tiles, used in body armour systems, are currently inspected visually offline using an X-ray technique that is both time consuming and very expensive. The aim of this research is to develop a methodology to detect, locate and classify various manufacturing defects in Reaction Sintered Silicon Carbide (RSSC) ceramic tiles, using an ultrasonic sensing technique. Defects such as free silicon, un-sintered silicon carbide material and conventional porosity are often difficult to detect using conventional X-radiography. An alternative inspection system was developed to detect defects in ceramic components using an Artificial Neural Network (ANN) based signal processing technique. The inspection methodology proposed focuses on pre-processing of signals, de-noising, wavelet decomposition, feature extraction and post-processing of the signals for classification purposes. This research contributes to developing an on-line inspection system that would be far more cost effective than present methods and, moreover, assist manufacturers in checking the location of high density areas, defects and enable real time quality control, including the implementation of accept/reject criteria. Copyright © 2013 Elsevier B.V. All rights reserved.

Automatic Building Damage Detection Method Using High-Resolution Remote Sensing Images and 3d GIS Model

NASA Astrophysics Data System (ADS)

Tu, Jihui; Sui, Haigang; Feng, Wenqing; Song, Zhina

2016-06-01

In this paper, a novel approach of building damaged detection is proposed using high resolution remote sensing images and 3D GIS-Model data. Traditional building damage detection method considers to detect damaged building due to earthquake, but little attention has been paid to analyze various building damaged types(e.g., trivial damaged, severely damaged and totally collapsed.) Therefore, we want to detect the different building damaged type using 2D and 3D feature of scenes because the real world we live in is a 3D space. The proposed method generalizes that the image geometric correction method firstly corrects the post-disasters remote sensing image using the 3D GIS model or RPC parameters, then detects the different building damaged types using the change of the height and area between the pre- and post-disasters and the texture feature of post-disasters. The results, evaluated on a selected study site of the Beichuan earthquake ruins, Sichuan, show that this method is feasible and effective in building damage detection. It has also shown that the proposed method is easily applicable and well suited for rapid damage assessment after natural disasters.

A land cover change detection and classification protocol for updating Alaska NLCD 2001 to 2011

USGS Publications Warehouse

Jin, Suming; Yang, Limin; Zhu, Zhe; Homer, Collin G.

2017-01-01

Monitoring and mapping land cover changes are important ways to support evaluation of the status and transition of ecosystems. The Alaska National Land Cover Database (NLCD) 2001 was the first 30-m resolution baseline land cover product of the entire state derived from circa 2001 Landsat imagery and geospatial ancillary data. We developed a comprehensive approach named AKUP11 to update Alaska NLCD from 2001 to 2011 and provide a 10-year cyclical update of the state's land cover and land cover changes. Our method is designed to characterize the main land cover changes associated with different drivers, including the conversion of forests to shrub and grassland primarily as a result of wildland fire and forest harvest, the vegetation successional processes after disturbance, and changes of surface water extent and glacier ice/snow associated with weather and climate changes. For natural vegetated areas, a component named AKUP11-VEG was developed for updating the land cover that involves four major steps: 1) identify the disturbed and successional areas using Landsat images and ancillary datasets; 2) update the land cover status for these areas using a SKILL model (System of Knowledge-based Integrated-trajectory Land cover Labeling); 3) perform decision tree classification; and 4) develop a final land cover and land cover change product through the postprocessing modeling. For water and ice/snow areas, another component named AKUP11-WIS was developed for initial land cover change detection, removal of the terrain shadow effects, and exclusion of ephemeral snow changes using a 3-year MODIS snow extent dataset from 2010 to 2012. The overall approach was tested in three pilot study areas in Alaska, with each area consisting of four Landsat image footprints. The results from the pilot study show that the overall accuracy in detecting change and no-change is 90% and the overall accuracy of the updated land cover label for 2011 is 86%. The method provided a robust, consistent, and efficient means for capturing major disturbance events and updating land cover for Alaska. The method has subsequently been applied to generate the land cover and land cover change products for the entire state of Alaska.

76 FR 21413 - Classification Changes for Competitive Mail Services

Federal Register 2010, 2011, 2012, 2013, 2014

2011-04-15

... POSTAL REGULATORY COMMISSION [Docket No. MC2011-24; Order No. 714] Classification Changes for... noticing a recently-filed Postal Service notice of two classification changes concerning certain... of two classification changes pursuant to 39 CFR 3020.90 and 3020.91 concerning certain competitive...

Single-trial lie detection using a combined fNIRS-polygraph system

PubMed Central

Bhutta, M. Raheel; Hong, Melissa J.; Kim, Yun-Hee; Hong, Keum-Shik

2015-01-01

Deception is a human behavior that many people experience in daily life. It involves complex neuronal activities in addition to several physiological changes in the body. A polygraph, which can measure some of the physiological responses from the body, has been widely employed in lie-detection. Many researchers, however, believe that lie detection can become more precise if the neuronal changes that occur in the process of deception can be isolated and measured. In this study, we combine both measures (i.e., physiological and neuronal changes) for enhanced lie-detection. Specifically, to investigate the deception-related hemodynamic response, functional near-infrared spectroscopy (fNIRS) is applied at the prefrontal cortex besides a commercially available polygraph system. A mock crime scenario with a single-trial stimulus is set up as a deception protocol. The acquired data are classified into “true” and “lie” classes based on the fNIRS-based hemoglobin-concentration changes and polygraph-based physiological signal changes. Linear discriminant analysis is utilized as a classifier. The results indicate that the combined fNIRS-polygraph system delivers much higher classification accuracy than that of a singular system. This study demonstrates a plausible solution toward single-trial lie-detection by combining fNIRS and the polygraph. PMID:26082733

Methods of practice and guidelines for using survey-grade global navigation satellite systems (GNSS) to establish vertical datum in the United States Geological Survey

USGS Publications Warehouse

Rydlund, Jr., Paul H.; Densmore, Brenda K.

2012-01-01

Geodetic surveys have evolved through the years to the use of survey-grade (centimeter level) global positioning to perpetuate and post-process vertical datum. The U.S. Geological Survey (USGS) uses Global Navigation Satellite Systems (GNSS) technology to monitor natural hazards, ensure geospatial control for climate and land use change, and gather data necessary for investigative studies related to water, the environment, energy, and ecosystems. Vertical datum is fundamental to a variety of these integrated earth sciences. Essentially GNSS surveys provide a three-dimensional position x, y, and z as a function of the North American Datum of 1983 ellipsoid and the most current hybrid geoid model. A GNSS survey may be approached with post-processed positioning for static observations related to a single point or network, or involve real-time corrections to provide positioning "on-the-fly." Field equipment required to facilitate GNSS surveys range from a single receiver, with a power source for static positioning, to an additional receiver or network communicated by radio or cellular for real-time positioning. A real-time approach in its most common form may be described as a roving receiver augmented by a single-base station receiver, known as a single-base real-time (RT) survey. More efficient real-time methods involving a Real-Time Network (RTN) permit the use of only one roving receiver that is augmented to a network of fixed receivers commonly known as Continually Operating Reference Stations (CORS). A post-processed approach in its most common form involves static data collection at a single point. Data are most commonly post-processed through a universally accepted utility maintained by the National Geodetic Survey (NGS), known as the Online Position User Service (OPUS). More complex post-processed methods involve static observations among a network of additional receivers collecting static data at known benchmarks. Both classifications provide users flexibility regarding efficiency and quality of data collection. Quality assurance of survey-grade global positioning is often overlooked or not understood and perceived uncertainties can be misleading. GNSS users can benefit from a blueprint of data collection standards used to ensure consistency among USGS mission areas. A classification of GNSS survey qualities provide the user with the ability to choose from the highest quality survey used to establish objective points with low uncertainties, identified as a Level I, to a GNSS survey for general topographic control without quality assurance, identified as a Level IV. A Level I survey is strictly limited to post-processed methods, whereas Level II, Level III, and Level IV surveys integrate variations of a RT approach. Among these classifications, techniques involving blunder checks and redundancy are important, and planning that involves the assessment of the overall satellite configuration, as well as terrestrial and space weather, are necessary to ensure an efficient and quality campaign. Although quality indicators and uncertainties are identified in post-processed methods using CORS, the accuracy of a GNSS survey is most effectively expressed as a comparison to a local benchmark that has a high degree of confidence. Real-time and post-processed methods should incorporate these "trusted" benchmarks as a check during any campaign. Global positioning surveys are expected to change rapidly in the future. The expansion of continuously operating reference stations, combined with newly available satellite signals, and enhancements to the conterminous geoid, are all sufficient indicators for substantial growth in real-time positioning and quality thereof.

Classification of brain tumors using texture based analysis of T1-post contrast MR scans in a preclinical model

NASA Astrophysics Data System (ADS)

Tang, Tien T.; Zawaski, Janice A.; Francis, Kathleen N.; Qutub, Amina A.; Gaber, M. Waleed

2018-02-01

Accurate diagnosis of tumor type is vital for effective treatment planning. Diagnosis relies heavily on tumor biopsies and other clinical factors. However, biopsies do not fully capture the tumor's heterogeneity due to sampling bias and are only performed if the tumor is accessible. An alternative approach is to use features derived from routine diagnostic imaging such as magnetic resonance (MR) imaging. In this study we aim to establish the use of quantitative image features to classify brain tumors and extend the use of MR images beyond tumor detection and localization. To control for interscanner, acquisition and reconstruction protocol variations, the established workflow was performed in a preclinical model. Using glioma (U87 and GL261) and medulloblastoma (Daoy) models, T1-weighted post contrast scans were acquired at different time points post-implant. The tumor regions at the center, middle, and peripheral were analyzed using in-house software to extract 32 different image features consisting of first and second order features. The extracted features were used to construct a decision tree, which could predict tumor type with 10-fold cross-validation. Results from the final classification model demonstrated that middle tumor region had the highest overall accuracy at 79%, while the AUC accuracy was over 90% for GL261 and U87 tumors. Our analysis further identified image features that were unique to certain tumor region, although GL261 tumors were more homogenous with no significant differences between the central and peripheral tumor regions. In conclusion our study shows that texture features derived from MR scans can be used to classify tumor type with high success rates. Furthermore, the algorithm we have developed can be implemented with any imaging datasets and may be applicable to multiple tumor types to determine diagnosis.

Children exposed to disaster: I. Epidemiology of post-traumatic symptoms and symptom profiles.

PubMed

Shannon, M P; Lonigan, C J; Finch, A J; Taylor, C M

1994-01-01

To determine the range and severity of post-traumatic stress disorder (PTSD) symptoms exhibited by children after exposure to a natural disaster. Three months after Hurricane Hugo struck Berkeley County, South Carolina, 5,687 school-aged children were surveyed about their experiences and reactions related to the storm. Self-reports of PTSD symptoms were obtained by use of a PTSD Reaction Index. Significant variation in the prevalence of PTSD symptoms was found across race, gender, and age groups. Self-reported symptoms were used to derive a post-traumatic stress syndrome classification according to DSM-III-R guidelines for the diagnosis of PTSD. More than 5% of the sample reported sufficient symptoms to be classified as exhibiting this post-traumatic stress syndrome. Females and younger children were more likely to receive this classification. At the symptom level, females reported more symptoms associated with emotional processing and emotional reaction to the trauma. Males were more likely to report symptoms related to cognitive and behavioral factors. Younger children were more likely to report symptoms overall. Children exposed to a high magnitude natural disaster report sufficient symptoms to establish a DSM-III-R derived classification of a PTSD syndrome. Differences between gender, age, and race groups appear to be related to differential risk of exposure, reporting biases, as well as a differential risk for developing post-traumatic symptoms.

Detecting post-fire salvage logging with Landsat change maps and national fire survey data

Treesearch

Todd A. Schroeder; Michael A. Wulder; Sean P. Healey; Gretchen G. Moisen

2012-01-01

In Canadian boreal forests, wildfire is the predominant agent of natural disturbance often with millions of hectares burning annually. In addition to fire, nearly one quarter of Canada's boreal forest is also managed for industrial wood production. Post-fire logging (or salvage harvesting) is increasingly used to minimize economic losses from fire, notwithstanding...

Inversion of High Frequency Acoustic Data for Sediment Properties Needed for the Detection and Classification of UXOs

DTIC Science & Technology

2015-05-26

FINAL REPORT Inversion of High Frequency Acoustic Data for Sediment Properties Needed for the Detection and Classification of UXOs SERDP...DOCUMENTATION PAGE Prescribed by ANSI Std. Z39.18 Form Approved OMB No. 0704-0188 The public reporting burden for this collection of information is...2015 Inversion of High Frequency Acoustic Data for Sediment Properties Needed for the Detection and Classification of UXO’s W912HQ-12-C-0049 MR

76 FR 47614 - Mail Classification Change

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-05

... POSTAL REGULATORY COMMISSION [Docket No. MC2011-27; Order No. 785] Mail Classification Change...-filed Postal Service request for a change in classification to the ``Reply Rides Free'' program. The... Service filed a notice of classification change pursuant to 39 CFR 3020.90 and 3020.91 concerning the...

A model for anomaly classification in intrusion detection systems

NASA Astrophysics Data System (ADS)

Ferreira, V. O.; Galhardi, V. V.; Gonçalves, L. B. L.; Silva, R. C.; Cansian, A. M.

2015-09-01

Intrusion Detection Systems (IDS) are traditionally divided into two types according to the detection methods they employ, namely (i) misuse detection and (ii) anomaly detection. Anomaly detection has been widely used and its main advantage is the ability to detect new attacks. However, the analysis of anomalies generated can become expensive, since they often have no clear information about the malicious events they represent. In this context, this paper presents a model for automated classification of alerts generated by an anomaly based IDS. The main goal is either the classification of the detected anomalies in well-defined taxonomies of attacks or to identify whether it is a false positive misclassified by the IDS. Some common attacks to computer networks were considered and we achieved important results that can equip security analysts with best resources for their analyses.

A review of the automated detection and classification of acute leukaemia: Coherent taxonomy, datasets, validation and performance measurements, motivation, open challenges and recommendations.

PubMed

Alsalem, M A; Zaidan, A A; Zaidan, B B; Hashim, M; Madhloom, H T; Azeez, N D; Alsyisuf, S

2018-05-01

Acute leukaemia diagnosis is a field requiring automated solutions, tools and methods and the ability to facilitate early detection and even prediction. Many studies have focused on the automatic detection and classification of acute leukaemia and their subtypes to promote enable highly accurate diagnosis. This study aimed to review and analyse literature related to the detection and classification of acute leukaemia. The factors that were considered to improve understanding on the field's various contextual aspects in published studies and characteristics were motivation, open challenges that confronted researchers and recommendations presented to researchers to enhance this vital research area. We systematically searched all articles about the classification and detection of acute leukaemia, as well as their evaluation and benchmarking, in three main databases: ScienceDirect, Web of Science and IEEE Xplore from 2007 to 2017. These indices were considered to be sufficiently extensive to encompass our field of literature. Based on our inclusion and exclusion criteria, 89 articles were selected. Most studies (58/89) focused on the methods or algorithms of acute leukaemia classification, a number of papers (22/89) covered the developed systems for the detection or diagnosis of acute leukaemia and few papers (5/89) presented evaluation and comparative studies. The smallest portion (4/89) of articles comprised reviews and surveys. Acute leukaemia diagnosis, which is a field requiring automated solutions, tools and methods, entails the ability to facilitate early detection or even prediction. Many studies have been performed on the automatic detection and classification of acute leukaemia and their subtypes to promote accurate diagnosis. Research areas on medical-image classification vary, but they are all equally vital. We expect this systematic review to help emphasise current research opportunities and thus extend and create additional research fields. Copyright © 2018 Elsevier B.V. All rights reserved.

Quantitative Assessment of Spatio-Temporal Desertification Rates in Azerbaijan during Using Timeseries Landsat-8 Satellite Images

NASA Astrophysics Data System (ADS)

Bayramov, Emil; Mammadov, Ramiz

2016-07-01

The main goals of this research are the object-based landcover classification of LANDSAT-8 multi-spectral satellite images in 2014 and 2015, quantification of Normalized Difference Vegetation Indices (NDVI) rates within the land-cover classes, change detection analysis between the NDVIs derived from multi-temporal LANDSAT-8 satellite images and the quantification of those changes within the land-cover classes and detection of changes between land-cover classes. The object-based classification accuracy of the land-cover classes was validated through the standard confusion matrix which revealed 80 % of land-cover classification accuracy for both years. The analysis revealed that the area of agricultural lands increased from 30911 sq. km. in 2014 to 31999 sq. km. in 2015. The area of barelands increased from 3933 sq. km. in 2014 to 4187 sq. km. in 2015. The area of forests increased from 8211 sq. km. in 2014 to 9175 sq. km. in 2015. The area of grasslands decreased from 27176 sq. km. in 2014 to 23294 sq. km. in 2015. The area of urban areas increased from 12479 sq. km. in 2014 to 12956 sq. km. in 2015. The decrease in the area of grasslands was mainly explained by the landuse shifts of grasslands to agricultural and urban lands. The quantification of low and medium NDVI rates revealed the increase within the agricultural, urban and forest land-cover classes in 2015. However, the high NDVI rates within agricultural, urban and forest land-cover classes in 2015 revealed to be lower relative to 2014. The change detection analysis between landscover types of 2014 and 2015 allowed to determine that 7740 sq. km. of grasslands shifted to agricultural landcover type whereas 5442sq. km. of agricultural lands shifted to rangelands. This means that the spatio-temporal patters of agricultural activities occurred in Azerbaijan because some of the areas reduced agricultural activities whereas some of them changed their landuse type to agricultural. Based on the achieved results, it is possible to conclude that the area of agricultural lands in Azerbaijan increased from 2014 to 2015. The crop productivity also increased in the croplands, however some of the areas showed lower productivity in 2015 relative to 2014.

Classification of Chronic Obstructive Pulmonary Disease (COPD) according to the new Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2017: Comparison with GOLD 2011.

PubMed

Marçôa, Raquel; Rodrigues, Daniela Marta; Dias, Margarida; Ladeira, Inês; Vaz, Ana Paula; Lima, Ricardo; Guimarães, Miguel

2018-02-01

Chronic Obstructive Pulmonary Disease (COPD) is a major cause of morbidity and mortality worldwide. The Global Initiative for Chronic Obstructive Lung Disease (GOLD) project has been working to improve awareness, prevention and management of this disease. The aim of this study is to evaluate how COPD patients are reclassified by the 2017 GOLD system (versus GOLD 2011), to calculate the level of agreement between these two classifications in allocation to categories and to compare the performance of each classification to predict future exacerbations. Two-hundred COPD patients (>40 years, post bronchodilator forced expiratory volume in one second/forced vital capacity<0.7) followed in pulmonology consultation were recruited into this prospective multicentric study. Approximately half of the patients classified as GOLD D [2011] changed to GOLD B [2017]. The extent of agreement between GOLD 2011 and GOLD 2017 was moderate (Cohen's Kappa = 0.511; p < 0.001) and the ability to predict exacerbations was similar (69.7% and 67.6%, respectively). GOLD B [2017] exacerbated 17% more than GOLD B [2011] and had a lower percent predicted post bronchodilator forced expiratory volume in one second (FEV1). GOLD B [2017] turned to be the predominant category, more heterogeneous and with a higher risk of exacerbation versus GOLD B [2011]. Physicians should be cautious in assessing the GOLD B [2017] patients. The assessment of patients should always be personalized. More studies are needed to evaluate the impact of the 2017 reclassification in predicting outcomes such as future exacerbations and mortality.

Android malware detection based on evolutionary super-network

NASA Astrophysics Data System (ADS)

Yan, Haisheng; Peng, Lingling

2018-04-01

In the paper, an android malware detection method based on evolutionary super-network is proposed in order to improve the precision of android malware detection. Chi square statistics method is used for selecting characteristics on the basis of analyzing android authority. Boolean weighting is utilized for calculating characteristic weight. Processed characteristic vector is regarded as the system training set and test set; hyper edge alternative strategy is used for training super-network classification model, thereby classifying test set characteristic vectors, and it is compared with traditional classification algorithm. The results show that the detection method proposed in the paper is close to or better than traditional classification algorithm. The proposed method belongs to an effective Android malware detection means.

Steganalysis feature improvement using expectation maximization

NASA Astrophysics Data System (ADS)

Rodriguez, Benjamin M.; Peterson, Gilbert L.; Agaian, Sos S.

2007-04-01

Images and data files provide an excellent opportunity for concealing illegal or clandestine material. Currently, there are over 250 different tools which embed data into an image without causing noticeable changes to the image. From a forensics perspective, when a system is confiscated or an image of a system is generated the investigator needs a tool that can scan and accurately identify files suspected of containing malicious information. The identification process is termed the steganalysis problem which focuses on both blind identification, in which only normal images are available for training, and multi-class identification, in which both the clean and stego images at several embedding rates are available for training. In this paper an investigation of a clustering and classification technique (Expectation Maximization with mixture models) is used to determine if a digital image contains hidden information. The steganalysis problem is for both anomaly detection and multi-class detection. The various clusters represent clean images and stego images with between 1% and 10% embedding percentage. Based on the results it is concluded that the EM classification technique is highly suitable for both blind detection and the multi-class problem.

Detection, recognition, identification, and tracking of military vehicles using biomimetic intelligence

NASA Astrophysics Data System (ADS)

Pace, Paul W.; Sutherland, John

2001-10-01

This project is aimed at analyzing EO/IR images to provide automatic target detection/recognition/identification (ATR/D/I) of militarily relevant land targets. An increase in performance was accomplished using a biomimetic intelligence system functioning on low-cost, commercially available processing chips. Biomimetic intelligence has demonstrated advanced capabilities in the areas of hand- printed character recognition, real-time detection/identification of multiple faces in full 3D perspectives in cluttered environments, advanced capabilities in classification of ground-based military vehicles from SAR, and real-time ATR/D/I of ground-based military vehicles from EO/IR/HRR data in cluttered environments. The investigation applied these tools to real data sets and examined the parameters such as the minimum resolution for target recognition, the effect of target size, rotation, line-of-sight changes, contrast, partial obscuring, background clutter etc. The results demonstrated a real-time ATR/D/I capability against a subset of militarily relevant land targets operating in a realistic scenario. Typical results on the initial EO/IR data indicate probabilities of correct classification of resolved targets to be greater than 95 percent.

"How Did We Get Here?": Topic Drift in Online Health Discussions.

PubMed

Park, Albert; Hartzler, Andrea L; Huh, Jina; Hsieh, Gary; McDonald, David W; Pratt, Wanda

2016-11-02

Patients increasingly use online health communities to exchange health information and peer support. During the progression of health discussions, a change of topic-topic drift-can occur. Topic drift is a frequent phenomenon linked to incoherence and frustration in online communities and other forms of computer-mediated communication. For sensitive topics, such as health, such drift could have life-altering repercussions, yet topic drift has not been studied in these contexts. Our goals were to understand topic drift in online health communities and then to develop and evaluate an automated approach to detect both topic drift and efforts of community members to counteract such drift. We manually analyzed 721 posts from 184 threads from 7 online health communities within WebMD to understand topic drift, members' reaction towards topic drift, and their efforts to counteract topic drift. Then, we developed an automated approach to detect topic drift and counteraction efforts. We detected topic drift by calculating cosine similarity between 229,156 posts from 37,805 threads and measuring change of cosine similarity scores from the threads' first posts to their sequential posts. Using a similar approach, we detected counteractions to topic drift in threads by focusing on the irregular increase of similarity scores compared to the previous post in threads. Finally, we evaluated the performance of our automated approaches to detect topic drift and counteracting efforts by using a manually developed gold standard. Our qualitative analyses revealed that in threads of online health communities, topics change gradually, but usually stay within the global frame of topics for the specific community. Members showed frustration when topic drift occurred in the middle of threads but reacted positively to off-topic stories shared as separate threads. Although all types of members helped to counteract topic drift, original posters provided the most effort to keep threads on topic. Cosine similarity scores show promise for automatically detecting topical changes in online health discussions. In our manual evaluation, we achieved an F1 score of .71 and .73 for detecting topic drift and counteracting efforts to stay on topic, respectively. Our analyses expand our understanding of topic drift in a health context and highlight practical implications, such as promoting off-topic discussions as a function of building rapport in online health communities. Furthermore, the quantitative findings suggest that an automated tool could help detect topic drift, support counteraction efforts to bring the conversation back on topic, and improve communication in these important communities. Findings from this study have the potential to reduce topic drift and improve online health community members' experience of computer-mediated communication. Improved communication could enhance the personal health management of members who seek essential information and support during times of difficulty. ©Albert Park, Andrea L Hartzler, Jina Huh, Gary Hsieh, David W McDonald, Wanda Pratt. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 02.11.2016.

Progress in the detection of neoplastic progress and cancer by Raman spectroscopy

NASA Astrophysics Data System (ADS)

Bakker Schut, Tom C.; Stone, Nicholas; Kendall, Catherine A.; Barr, Hugh; Bruining, Hajo A.; Puppels, Gerwin J.

2000-05-01

Early detection of cancer is important because of the improved survival rates when the cancer is treated early. We study the application of NIR Raman spectroscopy for detection of dysplasia because this technique is sensitive to the small changes in molecular invasive in vivo detection using fiber-optic probes. The result of an in vitro study to detect neoplastic progress of esophageal Barrett's esophageal tissue will be presented. Using multivariate statistics, we developed three different linear discriminant analysis classification models to predict tissue type on the basis of the measured spectrum. Spectra of normal, metaplastic and dysplasia tissue could be discriminated with an accuracy of up to 88 percent. Therefore Raman spectroscopy seems to be a very suitable technique to detect dysplasia in Barrett's esophageal tissue.

Topobathymetric LiDAR point cloud processing and landform classification in a tidal environment

NASA Astrophysics Data System (ADS)

Skovgaard Andersen, Mikkel; Al-Hamdani, Zyad; Steinbacher, Frank; Rolighed Larsen, Laurids; Brandbyge Ernstsen, Verner

2017-04-01

Historically it has been difficult to create high resolution Digital Elevation Models (DEMs) in land-water transition zones due to shallow water depth and often challenging environmental conditions. This gap of information has been reflected as a "white ribbon" with no data in the land-water transition zone. In recent years, the technology of airborne topobathymetric Light Detection and Ranging (LiDAR) has proven capable of filling out the gap by simultaneously capturing topographic and bathymetric elevation information, using only a single green laser. We collected green LiDAR point cloud data in the Knudedyb tidal inlet system in the Danish Wadden Sea in spring 2014. Creating a DEM from a point cloud requires the general processing steps of data filtering, water surface detection and refraction correction. However, there is no transparent and reproducible method for processing green LiDAR data into a DEM, specifically regarding the procedure of water surface detection and modelling. We developed a step-by-step procedure for creating a DEM from raw green LiDAR point cloud data, including a procedure for making a Digital Water Surface Model (DWSM) (see Andersen et al., 2017). Two different classification analyses were applied to the high resolution DEM: A geomorphometric and a morphological classification, respectively. The classification methods were originally developed for a small test area; but in this work, we have used the classification methods to classify the complete Knudedyb tidal inlet system. References Andersen MS, Gergely Á, Al-Hamdani Z, Steinbacher F, Larsen LR, Ernstsen VB (2017). Processing and performance of topobathymetric lidar data for geomorphometric and morphological classification in a high-energy tidal environment. Hydrol. Earth Syst. Sci., 21: 43-63, doi:10.5194/hess-21-43-2017. Acknowledgements This work was funded by the Danish Council for Independent Research | Natural Sciences through the project "Process-based understanding and prediction of morphodynamics in a natural coastal system in response to climate change" (Steno Grant no. 10-081102) and by the Geocenter Denmark through the project "Closing the gap! - Coherent land-water environmental mapping (LAWA)" (Grant no. 4-2015).

Resampling approach for anomalous change detection

NASA Astrophysics Data System (ADS)

Theiler, James; Perkins, Simon

2007-04-01

We investigate the problem of identifying pixels in pairs of co-registered images that correspond to real changes on the ground. Changes that are due to environmental differences (illumination, atmospheric distortion, etc.) or sensor differences (focus, contrast, etc.) will be widespread throughout the image, and the aim is to avoid these changes in favor of changes that occur in only one or a few pixels. Formal outlier detection schemes (such as the one-class support vector machine) can identify rare occurrences, but will be confounded by pixels that are "equally rare" in both images: they may be anomalous, but they are not changes. We describe a resampling scheme we have developed that formally addresses both of these issues, and reduces the problem to a binary classification, a problem for which a large variety of machine learning tools have been developed. In principle, the effects of misregistration will manifest themselves as pervasive changes, and our method will be robust against them - but in practice, misregistration remains a serious issue.

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

NASA Astrophysics Data System (ADS)

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

2010-02-01

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

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

NASA Astrophysics Data System (ADS)

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

2013-03-01

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

A study and evaluation of image analysis techniques applied to remotely sensed data

NASA Technical Reports Server (NTRS)

Atkinson, R. J.; Dasarathy, B. V.; Lybanon, M.; Ramapriyan, H. K.

1976-01-01

An analysis of phenomena causing nonlinearities in the transformation from Landsat multispectral scanner coordinates to ground coordinates is presented. Experimental results comparing rms errors at ground control points indicated a slight improvement when a nonlinear (8-parameter) transformation was used instead of an affine (6-parameter) transformation. Using a preliminary ground truth map of a test site in Alabama covering the Mobile Bay area and six Landsat images of the same scene, several classification methods were assessed. A methodology was developed for automatic change detection using classification/cluster maps. A coding scheme was employed for generation of change depiction maps indicating specific types of changes. Inter- and intraseasonal data of the Mobile Bay test area were compared to illustrate the method. A beginning was made in the study of data compression by applying a Karhunen-Loeve transform technique to a small section of the test data set. The second part of the report provides a formal documentation of the several programs developed for the analysis and assessments presented.

Surface EEG Shows that Functional Segregation via Phase Coupling Contributes to the Neural Substrate of Mental Calculations

ERIC Educational Resources Information Center

Dimitriadis, Stavros I.; Kanatsouli, Kassiani; Laskaris, Nikolaos A.; Tsirka, Vasso; Vourkas, Michael; Micheloyannis, Sifis

2012-01-01

Multichannel EEG traces from healthy subjects are used to investigate the brain's self-organisation tendencies during two different mental arithmetic tasks. By making a comparison with a control-state in the form of a classification problem, we can detect and quantify the changes in coordinated brain activity in terms of functional connectivity.…

Analysis of EEG-fMRI data in focal epilepsy based on automated spike classification and Signal Space Projection.

PubMed

Liston, Adam D; De Munck, Jan C; Hamandi, Khalid; Laufs, Helmut; Ossenblok, Pauly; Duncan, John S; Lemieux, Louis

2006-07-01

Simultaneous acquisition of EEG and fMRI data enables the investigation of the hemodynamic correlates of interictal epileptiform discharges (IEDs) during the resting state in patients with epilepsy. This paper addresses two issues: (1) the semi-automation of IED classification in statistical modelling for fMRI analysis and (2) the improvement of IED detection to increase experimental fMRI efficiency. For patients with multiple IED generators, sensitivity to IED-correlated BOLD signal changes can be improved when the fMRI analysis model distinguishes between IEDs of differing morphology and field. In an attempt to reduce the subjectivity of visual IED classification, we implemented a semi-automated system, based on the spatio-temporal clustering of EEG events. We illustrate the technique's usefulness using EEG-fMRI data from a subject with focal epilepsy in whom 202 IEDs were visually identified and then clustered semi-automatically into four clusters. Each cluster of IEDs was modelled separately for the purpose of fMRI analysis. This revealed IED-correlated BOLD activations in distinct regions corresponding to three different IED categories. In a second step, Signal Space Projection (SSP) was used to project the scalp EEG onto the dipoles corresponding to each IED cluster. This resulted in 123 previously unrecognised IEDs, the inclusion of which, in the General Linear Model (GLM), increased the experimental efficiency as reflected by significant BOLD activations. We have also shown that the detection of extra IEDs is robust in the face of fluctuations in the set of visually detected IEDs. We conclude that automated IED classification can result in more objective fMRI models of IEDs and significantly increased sensitivity.

Real-time classification of signals from three-component seismic sensors using neural nets

NASA Astrophysics Data System (ADS)

Bowman, B. C.; Dowla, F.

1992-05-01

Adaptive seismic data acquisition systems with capabilities of signal discrimination and event classification are important in treaty monitoring, proliferation, and earthquake early detection systems. Potential applications include monitoring underground chemical explosions, as well as other military, cultural, and natural activities where characteristics of signals change rapidly and without warning. In these applications, the ability to detect and interpret events rapidly without falling behind the influx of the data is critical. We developed a system for real-time data acquisition, analysis, learning, and classification of recorded events employing some of the latest technology in computer hardware, software, and artificial neural networks methods. The system is able to train dynamically, and updates its knowledge based on new data. The software is modular and hardware-independent; i.e., the front-end instrumentation is transparent to the analysis system. The software is designed to take advantage of the multiprocessing environment of the Unix operating system. The Unix System V shared memory and static RAM protocols for data access and the semaphore mechanism for interprocess communications were used. As the three-component sensor detects a seismic signal, it is displayed graphically on a color monitor using X11/Xlib graphics with interactive screening capabilities. For interesting events, the triaxial signal polarization is computed, a fast Fourier Transform (FFT) algorithm is applied, and the normalized power spectrum is transmitted to a backpropagation neural network for event classification. The system is currently capable of handling three data channels with a sampling rate of 500 Hz, which covers the bandwidth of most seismic events. The system has been tested in laboratory setting with artificial events generated in the vicinity of a three-component sensor.

Comparison of World Health Organization and Asia-Pacific body mass index classifications in COPD patients.

PubMed

Lim, Jeong Uk; Lee, Jae Ha; Kim, Ju Sang; Hwang, Yong Il; Kim, Tae-Hyung; Lim, Seong Yong; Yoo, Kwang Ha; Jung, Ki-Suck; Kim, Young Kyoon; Rhee, Chin Kook

2017-01-01

A low body mass index (BMI) is associated with increased mortality and low health-related quality of life in patients with COPD. The Asia-Pacific classification of BMI has a lower cutoff for overweight and obese categories compared to the World Health Organization (WHO) classification. The present study assessed patients with COPD among different BMI categories according to two BMI classification systems: WHO and Asia-Pacific. Patients with COPD aged 40 years or older from the Korean COPD Subtype Study cohort were selected for evaluation. We enrolled 1,462 patients. Medical history including age, sex, St George's Respiratory Questionnaire (SGRQ-C), the modified Medical Research Council (mMRC) dyspnea scale, and post-bronchodilator forced expiratory volume in 1 second (FEV 1 ) were evaluated. Patients were categorized into different BMI groups according to the two BMI classification systems. FEV 1 and the diffusing capacity of the lung for carbon monoxide (DLCO) percentage revealed an inverse "U"-shaped pattern as the BMI groups changed from underweight to obese when WHO cutoffs were applied. When Asia-Pacific cutoffs were applied, FEV 1 and DLCO (%) exhibited a linearly ascending relationship as the BMI increased, and the percentage of patients in the overweight and obese groups linearly decreased with increasing severity of the Global Initiative for Chronic Obstructive Lung Disease criteria. From the underweight to the overweight groups, SGRQ-C and mMRC had a decreasing relationship in both the WHO and Asia-Pacific classifications. The prevalence of comorbidities in the different BMI groups showed similar trends in both BMI classifications systems. The present study demonstrated that patients with COPD who have a high BMI have better pulmonary function and health-related quality of life and reduced dyspnea symptoms. Furthermore, the Asia-Pacific BMI classification more appropriately reflects the correlation of obesity and disease manifestation in Asian COPD patients than the WHO classification.

76 FR 29282 - Transfer of Post Office Box Service in Selected Locations to the Competitive Product List

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-20

... POSTAL SERVICE Transfer of Post Office Box Service in Selected Locations to the Competitive... service at approximately 6,800 locations from the Mail Classification Schedule's Market Dominant Product...] filed with the Postal Regulatory Commission a request to transfer Post Office Box service at locations...

Robust spike classification based on frequency domain neural waveform features.

PubMed

Yang, Chenhui; Yuan, Yuan; Si, Jennie

2013-12-01

We introduce a new spike classification algorithm based on frequency domain features of the spike snippets. The goal for the algorithm is to provide high classification accuracy, low false misclassification, ease of implementation, robustness to signal degradation, and objectivity in classification outcomes. In this paper, we propose a spike classification algorithm based on frequency domain features (CFDF). It makes use of frequency domain contents of the recorded neural waveforms for spike classification. The self-organizing map (SOM) is used as a tool to determine the cluster number intuitively and directly by viewing the SOM output map. After that, spike classification can be easily performed using clustering algorithms such as the k-Means. In conjunction with our previously developed multiscale correlation of wavelet coefficient (MCWC) spike detection algorithm, we show that the MCWC and CFDF detection and classification system is robust when tested on several sets of artificial and real neural waveforms. The CFDF is comparable to or outperforms some popular automatic spike classification algorithms with artificial and real neural data. The detection and classification of neural action potentials or neural spikes is an important step in single-unit-based neuroscientific studies and applications. After the detection of neural snippets potentially containing neural spikes, a robust classification algorithm is applied for the analysis of the snippets to (1) extract similar waveforms into one class for them to be considered coming from one unit, and to (2) remove noise snippets if they do not contain any features of an action potential. Usually, a snippet is a small 2 or 3 ms segment of the recorded waveform, and differences in neural action potentials can be subtle from one unit to another. Therefore, a robust, high performance classification system like the CFDF is necessary. In addition, the proposed algorithm does not require any assumptions on statistical properties of the noise and proves to be robust under noise contamination.

Bacteriological incidence in pneumonia patients with pulmonary emphysema: a bacterial floral analysis using the 16S ribosomal RNA gene in bronchoalveolar lavage fluid.

PubMed

Naito, Keisuke; Yamasaki, Kei; Yatera, Kazuhiro; Akata, Kentaro; Noguchi, Shingo; Kawanami, Toshinori; Fukuda, Kazumasa; Kido, Takashi; Ishimoto, Hiroshi; Mukae, Hiroshi

2017-01-01

Pulmonary emphysema is an important radiological finding in chronic obstructive pulmonary disease patients, but bacteriological differences in pneumonia patients according to the severity of emphysematous changes have not been reported. Therefore, we evaluated the bacteriological incidence in the bronchoalveolar lavage fluid (BALF) of pneumonia patients using cultivation and a culture-independent molecular method. Japanese patients with community-acquired pneumonia (83) and healthcare-associated pneumonia (94) between April 2010 and February 2014 were evaluated. The BALF obtained from pneumonia lesions was evaluated by both cultivation and a molecular method. In the molecular method, ~600 base pairs of bacterial 16S ribosomal RNA genes in the BALF were amplified by polymerase chain reaction, and clone libraries were constructed. The nucleotide sequences of 96 randomly selected colonies were determined, and a homology search was performed to identify the bacterial species. A qualitative radiological evaluation of pulmonary emphysema based on chest computed tomography (CT) images was performed using the Goddard classification. The severity of pulmonary emphysema based on the Goddard classification was none in 47.4% (84/177), mild in 36.2% (64/177), moderate in 10.2% (18/177), and severe in 6.2% (11/177). Using the culture-independent molecular method, Moraxella catarrhalis was significantly more frequently detected in moderate or severe emphysema patients than in patients with no or mild emphysematous changes. The detection rates of Haemophilus influenzae and Pseudomonas aeruginosa were unrelated to the severity of pulmonary emphysematous changes, and Streptococcus species - except for the S. anginosus group and S. pneumoniae - were detected more frequently using the molecular method we used for the BALF of patients with pneumonia than using culture methods. Our findings suggest that M. catarrhalis is more frequently detected in pneumonia patients with moderate or severe emphysema than in those with no or mild emphysematous changes on chest CT. M. catarrhalis may play a major role in patients with pneumonia complicating severe pulmonary emphysema.

40 CFR 164.25 - Filing copies of notification of intent to cancel registration or change classification or...

Code of Federal Regulations, 2010 CFR

2010-07-01

... intent to cancel registration or change classification or refusal to register, and statement of issues... copies of notification of intent to cancel registration or change classification or refusal to register... appropriate notice of intention to cancel, the notice of intention to change the classification or the...

Detecting and Attributing the Effects of Climate Change on the Distributions of Snake Species Over the Past 50 Years.

PubMed

Wu, Jianguo

2016-01-01

It is unclear whether the distributions of snakes have changed in association with climate change over the past years. We detected the distribution changes of snakes over the past 50 years and determined whether the changes could be attributed to recent climate change in China. Long-term records of the distribution of nine snake species in China, grey relationship analysis, fuzzy sets classification techniques, the consistency index, and attributed methods were used. Over the past 50 years, the distributions of snake species have changed in multiple directions, primarily shifting northwards, and most of the changes were related to the thermal index. Driven by climatic factors over the past 50 years, the distribution boundary and distribution centers of some species changed with the fluctuations. The observed and predicted changes in distribution were highly consistent for some snake species. The changes in the northern limits of distributions of nearly half of the species, as well as the southern and eastern limits, and the distribution centers of some snake species can be attributed to climate change.

Characterization of post-fire surface cover, soils, and burn severity at the Cerro Grande Fire, New Mexico, using hyperspectral and multispectral remote sensing

USGS Publications Warehouse

Kokaly, R.F.; Rockwell, B.W.; Haire, S.L.; King, T.V.V.

2007-01-01

Forest fires leave behind a changed ecosystem with a patchwork of surface cover that includes ash, charred organic matter, soils and soil minerals, and dead, damaged, and living vegetation. The distributions of these materials affect post-fire processes of erosion, nutrient cycling, and vegetation regrowth. We analyzed high spatial resolution (2.4??m pixel size) Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) data collected over the Cerro Grande fire, to map post-fire surface cover into 10 classes, including ash, soil minerals, scorched conifer trees, and green vegetation. The Cerro Grande fire occurred near Los Alamos, New Mexico, in May 2000. The AVIRIS data were collected September 3, 2000. The surface cover map revealed complex patterns of ash, iron oxide minerals, and clay minerals in areas of complete combustion. Scorched conifer trees, which retained dry needles heated by the fire but not fully combusted by the flames, were found to cover much of the post-fire landscape. These scorched trees were found in narrow zones at the edges of completely burned areas. A surface cover map was also made using Landsat Enhanced Thematic Mapper plus (ETM+) data, collected September 5, 2000, and a maximum likelihood, supervised classification. When compared to AVIRIS, the Landsat classification grossly overestimated cover by dry conifer and ash classes and severely underestimated soil and green vegetation cover. In a comparison of AVIRIS surface cover to the Burned Area Emergency Rehabilitation (BAER) map of burn severity, the BAER high burn severity areas did not capture the variable patterns of post-fire surface cover by ash, soil, and scorched conifer trees seen in the AVIRIS map. The BAER map, derived from air photos, also did not capture the distribution of scorched trees that were observed in the AVIRIS map. Similarly, the moderate severity class of Landsat-derived burn severity maps generated from the differenced Normalized Burn Ratio (dNBR) calculation had low agreement with the AVIRIS classes of scorched conifer trees. Burn severity and surface cover images were found to contain complementary information, with the dNBR map presenting an image of degree of change caused by fire and the AVIRIS-derived map showing specific surface cover resulting from fire.

Built-up Areas Extraction in High Resolution SAR Imagery based on the method of Multiple Feature Weighted Fusion

NASA Astrophysics Data System (ADS)

Liu, X.; Zhang, J. X.; Zhao, Z.; Ma, A. D.

2015-06-01

Synthetic aperture radar in the application of remote sensing technology is becoming more and more widely because of its all-time and all-weather operation, feature extraction research in high resolution SAR image has become a hot topic of concern. In particular, with the continuous improvement of airborne SAR image resolution, image texture information become more abundant. It's of great significance to classification and extraction. In this paper, a novel method for built-up areas extraction using both statistical and structural features is proposed according to the built-up texture features. First of all, statistical texture features and structural features are respectively extracted by classical method of gray level co-occurrence matrix and method of variogram function, and the direction information is considered in this process. Next, feature weights are calculated innovatively according to the Bhattacharyya distance. Then, all features are weighted fusion. At last, the fused image is classified with K-means classification method and the built-up areas are extracted after post classification process. The proposed method has been tested by domestic airborne P band polarization SAR images, at the same time, two groups of experiments based on the method of statistical texture and the method of structural texture were carried out respectively. On the basis of qualitative analysis, quantitative analysis based on the built-up area selected artificially is enforced, in the relatively simple experimentation area, detection rate is more than 90%, in the relatively complex experimentation area, detection rate is also higher than the other two methods. In the study-area, the results show that this method can effectively and accurately extract built-up areas in high resolution airborne SAR imagery.

Multi-scale Gaussian representation and outline-learning based cell image segmentation.

PubMed

Farhan, Muhammad; Ruusuvuori, Pekka; Emmenlauer, Mario; Rämö, Pauli; Dehio, Christoph; Yli-Harja, Olli

2013-01-01

High-throughput genome-wide screening to study gene-specific functions, e.g. for drug discovery, demands fast automated image analysis methods to assist in unraveling the full potential of such studies. Image segmentation is typically at the forefront of such analysis as the performance of the subsequent steps, for example, cell classification, cell tracking etc., often relies on the results of segmentation. We present a cell cytoplasm segmentation framework which first separates cell cytoplasm from image background using novel approach of image enhancement and coefficient of variation of multi-scale Gaussian scale-space representation. A novel outline-learning based classification method is developed using regularized logistic regression with embedded feature selection which classifies image pixels as outline/non-outline to give cytoplasm outlines. Refinement of the detected outlines to separate cells from each other is performed in a post-processing step where the nuclei segmentation is used as contextual information. We evaluate the proposed segmentation methodology using two challenging test cases, presenting images with completely different characteristics, with cells of varying size, shape, texture and degrees of overlap. The feature selection and classification framework for outline detection produces very simple sparse models which use only a small subset of the large, generic feature set, that is, only 7 and 5 features for the two cases. Quantitative comparison of the results for the two test cases against state-of-the-art methods show that our methodology outperforms them with an increase of 4-9% in segmentation accuracy with maximum accuracy of 93%. Finally, the results obtained for diverse datasets demonstrate that our framework not only produces accurate segmentation but also generalizes well to different segmentation tasks.

Multi-scale Gaussian representation and outline-learning based cell image segmentation

PubMed Central

2013-01-01

Background High-throughput genome-wide screening to study gene-specific functions, e.g. for drug discovery, demands fast automated image analysis methods to assist in unraveling the full potential of such studies. Image segmentation is typically at the forefront of such analysis as the performance of the subsequent steps, for example, cell classification, cell tracking etc., often relies on the results of segmentation. Methods We present a cell cytoplasm segmentation framework which first separates cell cytoplasm from image background using novel approach of image enhancement and coefficient of variation of multi-scale Gaussian scale-space representation. A novel outline-learning based classification method is developed using regularized logistic regression with embedded feature selection which classifies image pixels as outline/non-outline to give cytoplasm outlines. Refinement of the detected outlines to separate cells from each other is performed in a post-processing step where the nuclei segmentation is used as contextual information. Results and conclusions We evaluate the proposed segmentation methodology using two challenging test cases, presenting images with completely different characteristics, with cells of varying size, shape, texture and degrees of overlap. The feature selection and classification framework for outline detection produces very simple sparse models which use only a small subset of the large, generic feature set, that is, only 7 and 5 features for the two cases. Quantitative comparison of the results for the two test cases against state-of-the-art methods show that our methodology outperforms them with an increase of 4-9% in segmentation accuracy with maximum accuracy of 93%. Finally, the results obtained for diverse datasets demonstrate that our framework not only produces accurate segmentation but also generalizes well to different segmentation tasks. PMID:24267488

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

Pichara, Karim; Protopapas, Pavlos

We present an automatic classification method for astronomical catalogs with missing data. We use Bayesian networks and a probabilistic graphical model that allows us to perform inference to predict missing values given observed data and dependency relationships between variables. To learn a Bayesian network from incomplete data, we use an iterative algorithm that utilizes sampling methods and expectation maximization to estimate the distributions and probabilistic dependencies of variables from data with missing values. To test our model, we use three catalogs with missing data (SAGE, Two Micron All Sky Survey, and UBVI) and one complete catalog (MACHO). We examine howmore » classification accuracy changes when information from missing data catalogs is included, how our method compares to traditional missing data approaches, and at what computational cost. Integrating these catalogs with missing data, we find that classification of variable objects improves by a few percent and by 15% for quasar detection while keeping the computational cost the same.« less

Peer Review of Radiotherapy Planning: Quantifying Outcomes and a Proposal for Prospective Data Collection.

PubMed

Mackenzie, J; Graham, G; Olivotto, I A

2016-12-01

The Canadian Partnership for Quality Radiotherapy quality assurance guidelines recommend that radiation oncologist peer review of curative radiotherapy plans takes place ideally before the first fraction of treatment is delivered. This study documented and evaluated the outcomes of weekly, disease site-specific, radiotherapy peer review, quality assurance rounds at the Tom Baker Cancer Centre in Calgary, Canada with a view to making recommendations about the optimal timing and documentation of peer review during the radiotherapy planning processes. Outcomes of each case reviewed at (i) breast, (ii) head and neck (including thyroid and cutaneous cases) and (iii) lung team quality assurance rounds from 6 January to 5 May 2015 were recorded prospectively. Each radiotherapy plan was assigned an outcome: A for plans with no suggested changes; B for satisfactory, but where issues were raised to consider for future patients; or C when a change was recommended before the first or next fraction. The B outcomes were further subdivided into B1 for a case-specific concern and B2 for a policy gap. Plans were assessed after contour definition and before the plan was formulated (post-contouring reviews) and/or assessed when the plan was complete (post-planning reviews). 209 radiotherapy plans prescribed by 20 radiation oncologists were peer reviewed at 43 quality assurance meetings. 93% were curative-intent and 7% were palliative. 83% of plans were reviewed before delivery of the first treatment fraction. There were a total of 257 case reviews: 60 at the post-contouring stage, 197 at the post-planning stage, including 46 patients reviewed at both time points. Overall rates of A, B1, B2 and C outcomes were 78%, 9%, 4% and 9%, respectively. The most common reason for a B or C outcome was related to target volume definition. Only 56% of C outcomes at the post-planning stage would have been detected at the post-contouring stage. Results varied between tumour site groups. 9% of radiotherapy plans reviewed had changes suggested before delivery to the patient. Review at the post-planning stage after plan completion was necessary to detect all suggested changes, but for head and neck cases, all C outcomes could have been detected at the post-contouring stage. More widespread implementation of radiotherapy peer review in the UK is recommended. Copyright © 2016 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Epithelial cancer detection by oblique-incidence optical spectroscopy

NASA Astrophysics Data System (ADS)

Garcia-Uribe, Alejandro; Balareddy, Karthik C.; Zou, Jun; Wang, Kenneth K.; Duvic, Madeleine; Wang, Lihong V.

2009-02-01

This paper presents a study on non-invasive detection of two common epithelial cancers (skin and esophagus) based on oblique incidence diffuse reflectance spectroscopy (OIDRS). An OIDRS measurement system, which combines fiber optics and MEMS technologies, was developed. In our pilot studies, a total number of 137 cases have been measured in-vivo for skin cancer detection and a total number of 20 biopsy samples have been measured ex-vivo for esophageal cancer detection. To automatically differentiate the cancerous cases from benign ones, a statistical software classification program was also developed. An overall classification accuracy of 90% and 100% has been achieved for skin and esophageal cancer classification, respectively.

76 FR 46856 - Mail Classification Schedule Change

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-03

... POSTAL REGULATORY COMMISSION [Docket No. MC2011-26; Order No. 777] Mail Classification Schedule... recently-filed Postal Service request regarding classification changes to Priority Mail packaging. This...). SUPPLEMENTARY INFORMATION: On July 26, 2011, the Postal Service filed a notice of two classification changes...

Mangrove forest distributions and dynamics (1975–2005) of the tsunami-affected region of Asia

USGS Publications Warehouse

Giri, C.; Zhu, Z.; Tieszen, L.L.; Singh, A.; Gillette, S.; Kelmelis, J.A.

2008-01-01

Aim  We aimed to estimate the present extent of tsunami-affected mangrove forests and determine the rates and causes of deforestation from 1975 to 2005.Location  Our study region covers the tsunami-affected coastal areas of Indonesia, Malaysia, Thailand, Burma (Myanmar), Bangladesh, India and Sri Lanka in Asia.Methods  We interpreted time-series Landsat data using a hybrid supervised and unsupervised classification approach. Landsat data were geometrically corrected to an accuracy of plus-or-minus half a pixel, an accuracy necessary for change analysis. Each image was normalized for solar irradiance by converting digital number values to the top-of-the atmosphere reflectance. Ground truth data and existing maps and data bases were used to select training samples and also for iterative labelling. We used a post-classification change detection approach. Results were validated with the help of local experts and/or high-resolution commercial satellite data.Results  The region lost 12% of its mangrove forests from 1975 to 2005, to a present extent of c. 1,670,000 ha. Rates and causes of deforestation varied both spatially and temporally. Annual deforestation was highest in Burma (c. 1%) and lowest in Sri Lanka (0.1%). In contrast, mangrove forests in India and Bangladesh remained unchanged or gained a small percentage. Net deforestation peaked at 137,000 ha during 1990–2000, increasing from 97,000 ha during 1975–90, and declining to 14,000 ha during 2000–05. The major causes of deforestation were agricultural expansion (81%), aquaculture (12%) and urban development (2%).Main conclusions  We assessed and monitored mangrove forests in the tsunami-affected region of Asia using the historical archive of Landsat data. We also measured the rates of change and determined possible causes. The results of our study can be used to better understand the role of mangrove forests in saving lives and property from natural disasters such as the Indian Ocean tsunami, and to identify possible areas for conservation, restoration and rehabilitation.

Coastal Land-Use Dynamics in Southern Sonora, Mexico Between 1973-2001

NASA Astrophysics Data System (ADS)

Luers, A. L.; Seto, K. C.; Matson, P. A.; Matson, P. A.; Naylor, R.; Moreno, G. C.

2001-12-01

Human activities, such as urbanization, agriculture, and shrimp farming are dramatically changing the coastal landscape of southern Sonora, Mexico, and threatening the ecosystems goods and services these natural systems provide. In this study we investigate the trends of human-induced transformations of coastal lands between 1973 and the present. Subscenes from two mosaicked Landsat images from 1973 (MSS), 1986 (MSS), 1992 (MSS), 1994 (TM), 2000 (ETM), 2001 (ETM) were analyzed to evaluate land use and cover changes. We used a combination of supervised and unsupervised maximum likelihood classification to produce thematic land use and land cover maps for change detection and modeling. The results show that the most prevalent form of land-use change in the region over the study period has been the transformation of Pithaya forest and coastal wetlands to shrimp aquaculture. Shrimp farms that did not exist in the region in the early 1980s now represent over 12 percent of the study area. Our analysis suggests that this boom in shrimp farming was influenced by a series of policy reforms instituted by the Mexican Government over the last decade intended to open the rural economy to global markets. These reforms include modifications to the Fisheries, Foreign Investment and Land Tenure Laws, changes in the rural credit system, and liberalization of international trade policies (NAFTA). The data indicate overall increased rates of land conversion from natural covers (Pithaya forest, Mesquite forest, Choyal, salt flats) to human dominated ecosystems (aquaculture, agriculture, salt ponds, urban) in the post-reform period (1994 - 2001) compared to the pre-reform period (1973 - 1992). Our results highlight the importance of monitoring local impacts in evaluating national policies.