Prevalence of upper airway obstruction in patients with apparently asymptomatic euthyroid multi nodular goitre

PubMed Central

Menon, Sunil K.; Jagtap, Varsha S.; Sarathi, Vijaya; Lila, Anurag R.; Bandgar, Tushar R.; Menon, Padmavathy S; Shah, Nalini S.

2011-01-01

Aims: To study the prevalence of upper airway obstruction (UAO) in “apparently asymptomatic” patients with euthyroid multinodular goitre (MNG) and find correlation between clinical features, UAO on pulmonary function test (PFT) and tracheal narrowing on computerised tomography (CT). Materials and Methods: Consecutive patients with apparently asymptomatic euthyroid MNG attending thyroid clinic in a tertiary centre underwent clinical examination to elicit features of UAO, PFT, and CT of neck and chest. Statistical Analysis Used: Statistical analysis was done with SPSS version 11.5 using paired t-test, Chi square test, and Fisher's exact test. P value of <0.05 was considered to be significant. Results: Fifty-six patients (52 females and four males) were studied. The prevalence of UAO (PFT) and significant tracheal narrowing (CT) was 14.3%. and 9.3%, respectively. Clinical features failed to predict UAO or significant tracheal narrowing. Tracheal narrowing (CT) did not correlate with UAO (PFT). Volume of goitre significantly correlated with degree of tracheal narrowing. Conclusions: Clinical features do not predict UAO on PFT or tracheal narrowing on CT in apparently asymptomatic patients with euthyroid MNG. PMID:21966649

Hypothesis-Testing Demands Trustworthy Data—A Simulation Approach to Inferential Statistics Advocating the Research Program Strategy

PubMed Central

Krefeld-Schwalb, Antonia; Witte, Erich H.; Zenker, Frank

2018-01-01

In psychology as elsewhere, the main statistical inference strategy to establish empirical effects is null-hypothesis significance testing (NHST). The recent failure to replicate allegedly well-established NHST-results, however, implies that such results lack sufficient statistical power, and thus feature unacceptably high error-rates. Using data-simulation to estimate the error-rates of NHST-results, we advocate the research program strategy (RPS) as a superior methodology. RPS integrates Frequentist with Bayesian inference elements, and leads from a preliminary discovery against a (random) H0-hypothesis to a statistical H1-verification. Not only do RPS-results feature significantly lower error-rates than NHST-results, RPS also addresses key-deficits of a “pure” Frequentist and a standard Bayesian approach. In particular, RPS aggregates underpowered results safely. RPS therefore provides a tool to regain the trust the discipline had lost during the ongoing replicability-crisis. PMID:29740363

Hypothesis-Testing Demands Trustworthy Data-A Simulation Approach to Inferential Statistics Advocating the Research Program Strategy.

PubMed

Krefeld-Schwalb, Antonia; Witte, Erich H; Zenker, Frank

2018-01-01

In psychology as elsewhere, the main statistical inference strategy to establish empirical effects is null-hypothesis significance testing (NHST). The recent failure to replicate allegedly well-established NHST-results, however, implies that such results lack sufficient statistical power, and thus feature unacceptably high error-rates. Using data-simulation to estimate the error-rates of NHST-results, we advocate the research program strategy (RPS) as a superior methodology. RPS integrates Frequentist with Bayesian inference elements, and leads from a preliminary discovery against a (random) H 0 -hypothesis to a statistical H 1 -verification. Not only do RPS-results feature significantly lower error-rates than NHST-results, RPS also addresses key-deficits of a "pure" Frequentist and a standard Bayesian approach. In particular, RPS aggregates underpowered results safely. RPS therefore provides a tool to regain the trust the discipline had lost during the ongoing replicability-crisis.

Prediction of phenotypes of missense mutations in human proteins from biological assemblies.

PubMed

Wei, Qiong; Xu, Qifang; Dunbrack, Roland L

2013-02-01

Single nucleotide polymorphisms (SNPs) are the most frequent variation in the human genome. Nonsynonymous SNPs that lead to missense mutations can be neutral or deleterious, and several computational methods have been presented that predict the phenotype of human missense mutations. These methods use sequence-based and structure-based features in various combinations, relying on different statistical distributions of these features for deleterious and neutral mutations. One structure-based feature that has not been studied significantly is the accessible surface area within biologically relevant oligomeric assemblies. These assemblies are different from the crystallographic asymmetric unit for more than half of X-ray crystal structures. We find that mutations in the core of proteins or in the interfaces in biological assemblies are significantly more likely to be disease-associated than those on the surface of the biological assemblies. For structures with more than one protein in the biological assembly (whether the same sequence or different), we find the accessible surface area from biological assemblies provides a statistically significant improvement in prediction over the accessible surface area of monomers from protein crystal structures (P = 6e-5). When adding this information to sequence-based features such as the difference between wildtype and mutant position-specific profile scores, the improvement from biological assemblies is statistically significant but much smaller (P = 0.018). Combining this information with sequence-based features in a support vector machine leads to 82% accuracy on a balanced dataset of 50% disease-associated mutations from SwissVar and 50% neutral mutations from human/primate sequence differences in orthologous proteins. Copyright © 2012 Wiley Periodicals, Inc.

Weighted Feature Significance: A Simple, Interpretable Model of Compound Toxicity Based on the Statistical Enrichment of Structural Features

PubMed Central

Huang, Ruili; Southall, Noel; Xia, Menghang; Cho, Ming-Hsuang; Jadhav, Ajit; Nguyen, Dac-Trung; Inglese, James; Tice, Raymond R.; Austin, Christopher P.

2009-01-01

In support of the U.S. Tox21 program, we have developed a simple and chemically intuitive model we call weighted feature significance (WFS) to predict the toxicological activity of compounds, based on the statistical enrichment of structural features in toxic compounds. We trained and tested the model on the following: (1) data from quantitative high–throughput screening cytotoxicity and caspase activation assays conducted at the National Institutes of Health Chemical Genomics Center, (2) data from Salmonella typhimurium reverse mutagenicity assays conducted by the U.S. National Toxicology Program, and (3) hepatotoxicity data published in the Registry of Toxic Effects of Chemical Substances. Enrichments of structural features in toxic compounds are evaluated for their statistical significance and compiled into a simple additive model of toxicity and then used to score new compounds for potential toxicity. The predictive power of the model for cytotoxicity was validated using an independent set of compounds from the U.S. Environmental Protection Agency tested also at the National Institutes of Health Chemical Genomics Center. We compared the performance of our WFS approach with classical classification methods such as Naive Bayesian clustering and support vector machines. In most test cases, WFS showed similar or slightly better predictive power, especially in the prediction of hepatotoxic compounds, where WFS appeared to have the best performance among the three methods. The new algorithm has the important advantages of simplicity, power, interpretability, and ease of implementation. PMID:19805409

Asymmetric statistical features of the Chinese domestic and international gold price fluctuation

NASA Astrophysics Data System (ADS)

Cao, Guangxi; Zhao, Yingchao; Han, Yan

2015-05-01

Analyzing the statistical features of fluctuation is remarkably significant for financial risk identification and measurement. In this study, the asymmetric detrended fluctuation analysis (A-DFA) method was applied to evaluate asymmetric multifractal scaling behaviors in the Shanghai and New York gold markets. Our findings showed that the multifractal features of the Chinese and international gold spot markets were asymmetric. The gold return series persisted longer in an increasing trend than in a decreasing trend. Moreover, the asymmetric degree of multifractals in the Chinese and international gold markets decreased with the increase in fluctuation range. In addition, the empirical analysis using sliding window technology indicated that multifractal asymmetry in the Chinese and international gold markets was characterized by its time-varying feature. However, the Shanghai and international gold markets basically shared a similar asymmetric degree evolution pattern. The American subprime mortgage crisis (2008) and the European debt crisis (2010) enhanced the asymmetric degree of the multifractal features of the Chinese and international gold markets. Furthermore, we also make statistical tests for the results of multifractatity and asymmetry, and discuss the origin of them. Finally, results of the empirical analysis using the threshold autoregressive conditional heteroskedasticity (TARCH) and exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models exhibited that good news had a more significant effect on the cyclical fluctuation of the gold market than bad news. Moreover, good news exerted a more significant effect on the Chinese gold market than on the international gold market.

Automated thematic mapping and change detection of ERTS-A images. [digital interpretation of Arizona imagery

NASA Technical Reports Server (NTRS)

Gramenopoulos, N. (Principal Investigator)

1973-01-01

The author has identified the following significant results. For the recognition of terrain types, spatial signatures are developed from the diffraction patterns of small areas of ERTS-1 images. This knowledge is exploited for the measurements of a small number of meaningful spatial features from the digital Fourier transforms of ERTS-1 image cells containing 32 x 32 picture elements. Using these spatial features and a heuristic algorithm, the terrain types in the vicinity of Phoenix, Arizona were recognized by the computer with a high accuracy. Then, the spatial features were combined with spectral features and using the maximum likelihood criterion the recognition accuracy of terrain types increased substantially. It was determined that the recognition accuracy with the maximum likelihood criterion depends on the statistics of the feature vectors. Nonlinear transformations of the feature vectors are required so that the terrain class statistics become approximately Gaussian. It was also determined that for a given geographic area the statistics of the classes remain invariable for a period of a month but vary substantially between seasons.

A Guideline to Univariate Statistical Analysis for LC/MS-Based Untargeted Metabolomics-Derived Data

PubMed Central

Vinaixa, Maria; Samino, Sara; Saez, Isabel; Duran, Jordi; Guinovart, Joan J.; Yanes, Oscar

2012-01-01

Several metabolomic software programs provide methods for peak picking, retention time alignment and quantification of metabolite features in LC/MS-based metabolomics. Statistical analysis, however, is needed in order to discover those features significantly altered between samples. By comparing the retention time and MS/MS data of a model compound to that from the altered feature of interest in the research sample, metabolites can be then unequivocally identified. This paper reports on a comprehensive overview of a workflow for statistical analysis to rank relevant metabolite features that will be selected for further MS/MS experiments. We focus on univariate data analysis applied in parallel on all detected features. Characteristics and challenges of this analysis are discussed and illustrated using four different real LC/MS untargeted metabolomic datasets. We demonstrate the influence of considering or violating mathematical assumptions on which univariate statistical test rely, using high-dimensional LC/MS datasets. Issues in data analysis such as determination of sample size, analytical variation, assumption of normality and homocedasticity, or correction for multiple testing are discussed and illustrated in the context of our four untargeted LC/MS working examples. PMID:24957762

A Guideline to Univariate Statistical Analysis for LC/MS-Based Untargeted Metabolomics-Derived Data.

PubMed

Vinaixa, Maria; Samino, Sara; Saez, Isabel; Duran, Jordi; Guinovart, Joan J; Yanes, Oscar

2012-10-18

Several metabolomic software programs provide methods for peak picking, retention time alignment and quantification of metabolite features in LC/MS-based metabolomics. Statistical analysis, however, is needed in order to discover those features significantly altered between samples. By comparing the retention time and MS/MS data of a model compound to that from the altered feature of interest in the research sample, metabolites can be then unequivocally identified. This paper reports on a comprehensive overview of a workflow for statistical analysis to rank relevant metabolite features that will be selected for further MS/MS experiments. We focus on univariate data analysis applied in parallel on all detected features. Characteristics and challenges of this analysis are discussed and illustrated using four different real LC/MS untargeted metabolomic datasets. We demonstrate the influence of considering or violating mathematical assumptions on which univariate statistical test rely, using high-dimensional LC/MS datasets. Issues in data analysis such as determination of sample size, analytical variation, assumption of normality and homocedasticity, or correction for multiple testing are discussed and illustrated in the context of our four untargeted LC/MS working examples.

Predicting axillary lymph node metastasis from kinetic statistics of DCE-MRI breast images

NASA Astrophysics Data System (ADS)

Ashraf, Ahmed B.; Lin, Lilie; Gavenonis, Sara C.; Mies, Carolyn; Xanthopoulos, Eric; Kontos, Despina

2012-03-01

The presence of axillary lymph node metastases is the most important prognostic factor in breast cancer and can influence the selection of adjuvant therapy, both chemotherapy and radiotherapy. In this work we present a set of kinetic statistics derived from DCE-MRI for predicting axillary node status. Breast DCE-MRI images from 69 women with known nodal status were analyzed retrospectively under HIPAA and IRB approval. Axillary lymph nodes were positive in 12 patients while 57 patients had no axillary lymph node involvement. Kinetic curves for each pixel were computed and a pixel-wise map of time-to-peak (TTP) was obtained. Pixels were first partitioned according to the similarity of their kinetic behavior, based on TTP values. For every kinetic curve, the following pixel-wise features were computed: peak enhancement (PE), wash-in-slope (WIS), wash-out-slope (WOS). Partition-wise statistics for every feature map were calculated, resulting in a total of 21 kinetic statistic features. ANOVA analysis was done to select features that differ significantly between node positive and node negative women. Using the computed kinetic statistic features a leave-one-out SVM classifier was learned that performs with AUC=0.77 under the ROC curve, outperforming the conventional kinetic measures, including maximum peak enhancement (MPE) and signal enhancement ratio (SER), (AUCs of 0.61 and 0.57 respectively). These findings suggest that our DCE-MRI kinetic statistic features can be used to improve the prediction of axillary node status in breast cancer patients. Such features could ultimately be used as imaging biomarkers to guide personalized treatment choices for women diagnosed with breast cancer.

SU-D-207B-04: Morphological Features of MRI as a Correlate of Capsular Contracture in Breast Cancer Patients with Implant-Based Reconstructions

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

Tyagi, N; Sutton, E; Hunt, M

Purpose: Capsular contracture (CC) is a serious complication in patients receiving implant-based reconstruction for breast cancer. The goal of this study was to identify image-based correlates of CC using MRI imaging in breast cancer patients who received both MRI and clinical evaluation following reconstructive surgery. Methods: We analyzed a retrospective dataset of 50 patients who had both a diagnostic MR and a plastic surgeon’s evaluations of CC score (Baker’s score) within a six month period following mastectomy and reconstructive surgery. T2w sagittal MRIs (TR/TE = 3500/102 ms, slice thickness = 4 mm) were used for morphological shape features (roundness, eccentricity,more » solidity, extent and ratio-length) and histogram features (median, skewness and kurtosis) of the implant and the pectoralis muscle overlying the implant. Implant and pectoralis muscles were segmented in 3D using Computation Environment for Radiological Research (CERR) and shape and histogram features were calculated as a function of Baker’s score. Results: Shape features such as roundness and eccentricity were statistically significant in differentiating grade 1 and grade 2 (p = 0.009; p = 0.06) as well as grade 1 and grade 3 CC (p = 0.001; p = 0.006). Solidity and extent were statistically significant in differentiating grade 1 and grade 3 CC (p = 0.04; p = 0.04). Ratio-length was statistically significant in differentiating all grades of CC except grade 2 and grade 3 that showed borderline significance (p = 0.06). The muscle thickness, median intensity and kurtosis were significant in differentiating between grade 1 and grade 3 (p = 0.02), grade 1 and grade 2 (p = 0.03) and grade 1 and grade 3 (p = 0.01) respectively. Conclusion: Morphological shape features described on MR images were associated with the severity of CC. MRI may be important in objectively evaluating outcomes in breast cancer patients who undergo implant reconstruction.« less

TU-F-CAMPUS-J-05: Effect of Uncorrelated Noise Texture On Computed Tomography Quantitative Image Features

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

Oliver, J; Budzevich, M; Moros, E

Purpose: To investigate the relationship between quantitative image features (i.e. radiomics) and statistical fluctuations (i.e. electronic noise) in clinical Computed Tomography (CT) using the standardized American College of Radiology (ACR) CT accreditation phantom and patient images. Methods: Three levels of uncorrelated Gaussian noise were added to CT images of phantom and patients (20) acquired in static mode and respiratory tracking mode. We calculated the noise-power spectrum (NPS) of the original CT images of the phantom, and of the phantom images with added Gaussian noise with means of 50, 80, and 120 HU. Concurrently, on patient images (original and noise-added images),more » image features were calculated: 14 shape, 19 intensity (1st order statistics from intensity volume histograms), 18 GLCM features (2nd order statistics from grey level co-occurrence matrices) and 11 RLM features (2nd order statistics from run-length matrices). These features provide the underlying structural information of the images. GLCM (size 128x128) was calculated with a step size of 1 voxel in 13 directions and averaged. RLM feature calculation was performed in 13 directions with grey levels binning into 128 levels. Results: Adding the electronic noise to the images modified the quality of the NPS, shifting the noise from mostly correlated to mostly uncorrelated voxels. The dramatic increase in noise texture did not affect image structure/contours significantly for patient images. However, it did affect the image features and textures significantly as demonstrated by GLCM differences. Conclusion: Image features are sensitive to acquisition factors (simulated by adding uncorrelated Gaussian noise). We speculate that image features will be more difficult to detect in the presence of electronic noise (an uncorrelated noise contributor) or, for that matter, any other highly correlated image noise. This work focuses on the effect of electronic, uncorrelated, noise and future work shall examine the influence of changes in quantum noise on the features. J. Oliver was supported by NSF FGLSAMP BD award HRD #1139850 and the McKnight Doctoral Fellowship.« less

To Evaluate & Compare Retention of Complete Cast Crown in Natural Teeth Using Different Auxiliary Retentive Features with Two Different Crown Heights - An In Vitro Study.

PubMed

Vinaya, Kundapur; Rakshith, Hegde; Prasad D, Krishna; Manoj, Shetty; Sunil, Mankar; Naresh, Shetty

2015-06-01

To evaluate the retention of complete cast crowns in teeth with adequate and inadequate crown height and to evaluate the effects of auxiliary retentive features on retention form complete cast crowns. Sixty freshly extracted human premolars. They were divided into 2 major groups depending upon the height of the teeth after the preparation. Group1 (H1): prepared teeth with constant height of 3.5 mm and Group 2 (H2): prepared teeth with constant height of 2.5 mm. Each group is further subdivided into 3 subgroups, depending upon the retentive features incorporated. First sub group were prepared conventionally, second sub group with proximal grooves and third subgroups with proximal boxes preparation. Castings produced in Nickel chromium alloy were cemented with glass ionomer cement and the cemented castings were subjected to tensional forces required to dislodge each cemented casting from its preparation and used for comparison of retentive quality. The data obtained were statistically analyzed using Oneway ANOVA test. The results showed there was statistically significant difference between adequate (H1) and inadequate (H2) group and increase in retention when there was incorporation of retentive features compared to conventional preparations. Incorporation of retentive grooves was statistically significant compared to retention obtained by boxes. Results also showed there was no statistically significant difference between long conventional and short groove. Complete cast crowns on teeth with adequate crown height exhibited greater retention than with inadequate crown height. Proximal grooves provided greater amount of retention when compared with proximal boxes.

Detailed Spectral Analysis of the 260 ks XMM-Newton Data of 1E 1207.4-5209 and Significance of a 2.1 keV Absorption Feature

NASA Astrophysics Data System (ADS)

Mori, Kaya; Chonko, James C.; Hailey, Charles J.

2005-10-01

We have reanalyzed the 260 ks XMM-Newton observation of 1E 1207.4-5209. There are several significant improvements over previous work. First, a much broader range of physically plausible spectral models was used. Second, we have used a more rigorous statistical analysis. The standard F-distribution was not employed, but rather the exact finite statistics F-distribution was determined by Monte Carlo simulations. This approach was motivated by the recent work of Protassov and coworkers and Freeman and coworkers. They demonstrated that the standard F-distribution is not even asymptotically correct when applied to assess the significance of additional absorption features in a spectrum. With our improved analysis we do not find a third and fourth spectral feature in 1E 1207.4-5209 but only the two broad absorption features previously reported. Two additional statistical tests, one line model dependent and the other line model independent, confirmed our modified F-test analysis. For all physically plausible continuum models in which the weak residuals are strong enough to fit, the residuals occur at the instrument Au M edge. As a sanity check we confirmed that the residuals are consistent in strength and position with the instrument Au M residuals observed in 3C 273.

Learning discriminative functional network features of schizophrenia

NASA Astrophysics Data System (ADS)

Gheiratmand, Mina; Rish, Irina; Cecchi, Guillermo; Brown, Matthew; Greiner, Russell; Bashivan, Pouya; Polosecki, Pablo; Dursun, Serdar

2017-03-01

Associating schizophrenia with disrupted functional connectivity is a central idea in schizophrenia research. However, identifying neuroimaging-based features that can serve as reliable "statistical biomarkers" of the disease remains a challenging open problem. We argue that generalization accuracy and stability of candidate features ("biomarkers") must be used as additional criteria on top of standard significance tests in order to discover more robust biomarkers. Generalization accuracy refers to the utility of biomarkers for making predictions about individuals, for example discriminating between patients and controls, in novel datasets. Feature stability refers to the reproducibility of the candidate features across different datasets. Here, we extracted functional connectivity network features from fMRI data at both high-resolution (voxel-level) and a spatially down-sampled lower-resolution ("supervoxel" level). At the supervoxel level, we used whole-brain network links, while at the voxel level, due to the intractably large number of features, we sampled a subset of them. We compared statistical significance, stability and discriminative utility of both feature types in a multi-site fMRI dataset, composed of schizophrenia patients and healthy controls. For both feature types, a considerable fraction of features showed significant differences between the two groups. Also, both feature types were similarly stable across multiple data subsets. However, the whole-brain supervoxel functional connectivity features showed a higher cross-validation classification accuracy of 78.7% vs. 72.4% for the voxel-level features. Cross-site variability and heterogeneity in the patient samples in the multi-site FBIRN dataset made the task more challenging compared to single-site studies. The use of the above methodology in combination with the fully data-driven approach using the whole brain information have the potential to shed light on "biomarker discovery" in schizophrenia.

Applying quantitative adiposity feature analysis models to predict benefit of bevacizumab-based chemotherapy in ovarian cancer patients

NASA Astrophysics Data System (ADS)

Wang, Yunzhi; Qiu, Yuchen; Thai, Theresa; More, Kathleen; Ding, Kai; Liu, Hong; Zheng, Bin

2016-03-01

How to rationally identify epithelial ovarian cancer (EOC) patients who will benefit from bevacizumab or other antiangiogenic therapies is a critical issue in EOC treatments. The motivation of this study is to quantitatively measure adiposity features from CT images and investigate the feasibility of predicting potential benefit of EOC patients with or without receiving bevacizumab-based chemotherapy treatment using multivariate statistical models built based on quantitative adiposity image features. A dataset involving CT images from 59 advanced EOC patients were included. Among them, 32 patients received maintenance bevacizumab after primary chemotherapy and the remaining 27 patients did not. We developed a computer-aided detection (CAD) scheme to automatically segment subcutaneous fat areas (VFA) and visceral fat areas (SFA) and then extracted 7 adiposity-related quantitative features. Three multivariate data analysis models (linear regression, logistic regression and Cox proportional hazards regression) were performed respectively to investigate the potential association between the model-generated prediction results and the patients' progression-free survival (PFS) and overall survival (OS). The results show that using all 3 statistical models, a statistically significant association was detected between the model-generated results and both of the two clinical outcomes in the group of patients receiving maintenance bevacizumab (p<0.01), while there were no significant association for both PFS and OS in the group of patients without receiving maintenance bevacizumab. Therefore, this study demonstrated the feasibility of using quantitative adiposity-related CT image features based statistical prediction models to generate a new clinical marker and predict the clinical outcome of EOC patients receiving maintenance bevacizumab-based chemotherapy.

No-reference image quality assessment based on statistics of convolution feature maps

NASA Astrophysics Data System (ADS)

Lv, Xiaoxin; Qin, Min; Chen, Xiaohui; Wei, Guo

2018-04-01

We propose a Convolutional Feature Maps (CFM) driven approach to accurately predict image quality. Our motivation bases on the finding that the Nature Scene Statistic (NSS) features on convolution feature maps are significantly sensitive to distortion degree of an image. In our method, a Convolutional Neural Network (CNN) is trained to obtain kernels for generating CFM. We design a forward NSS layer which performs on CFM to better extract NSS features. The quality aware features derived from the output of NSS layer is effective to describe the distortion type and degree an image suffered. Finally, a Support Vector Regression (SVR) is employed in our No-Reference Image Quality Assessment (NR-IQA) model to predict a subjective quality score of a distorted image. Experiments conducted on two public databases demonstrate the promising performance of the proposed method is competitive to state of the art NR-IQA methods.

3D Texture Analysis in Renal Cell Carcinoma Tissue Image Grading

PubMed Central

Cho, Nam-Hoon; Choi, Heung-Kook

2014-01-01

One of the most significant processes in cancer cell and tissue image analysis is the efficient extraction of features for grading purposes. This research applied two types of three-dimensional texture analysis methods to the extraction of feature values from renal cell carcinoma tissue images, and then evaluated the validity of the methods statistically through grade classification. First, we used a confocal laser scanning microscope to obtain image slices of four grades of renal cell carcinoma, which were then reconstructed into 3D volumes. Next, we extracted quantitative values using a 3D gray level cooccurrence matrix (GLCM) and a 3D wavelet based on two types of basis functions. To evaluate their validity, we predefined 6 different statistical classifiers and applied these to the extracted feature sets. In the grade classification results, 3D Haar wavelet texture features combined with principal component analysis showed the best discrimination results. Classification using 3D wavelet texture features was significantly better than 3D GLCM, suggesting that the former has potential for use in a computer-based grading system. PMID:25371701

Refining clinical features and therapeutic options of new daily persistent headache: a retrospective study of 63 patients in India.

PubMed

Prakash, Sanjay; Saini, Samir; Rana, Kaushikkumar Ramanlal; Mahato, Pinaki

2012-08-01

The aim of this retrospective study was to provide data on the clinical features and treatment outcomes of patients with NDPH (fulfilling Kung et al.'s criteria). A total of 63 patients were observed during a 5-yr period (2007-2012). More than one-third (35 %) patients had migrainous features; 65 % patients fulfilled the ICHD-II criteria. Both groups were similar in most clinical and epidemiological features. However, migrainous features were more common in patients with a prior history of episodic migraine (though statistically not significant). After a median follow-up of 9 months, 37 % patients showed "excellent" response (no or less than 1 headache per month). Another 30 % patients had "good" response (>50 % reduction in headache frequency or days per month). Excellent response was more in patients with a history of less than 6 months duration (statistically not significant). Patients with a recognized trigger showed better prognosis. Response was better in patients who received intravenous therapy of methyl prednisolone and sodium valproate. We suggest prospective and controlled studies to confirm our observations.

Heterogeneity Between Ducts of the Same Nuclear Grade Involved by Duct Carcinoma In Situ (DCIS) of the Breast

PubMed Central

Miller, Naomi A.; Chapman, Judith-Anne W.; Qian, Jin; Christens-Barry, William A.; Fu, Yuejiao; Yuan, Yan; Lickley, H. Lavina A.; Axelrod, David E.

2010-01-01

Purpose Nuclear grade of breast DCIS is considered during patient management decision-making although it may have only a modest prognostic association with therapeutic outcome. We hypothesized that visual inspection may miss substantive differences in nuclei classified as having the same nuclear grade. To test this hypothesis, we measured subvisual nuclear features by quantitative image cytometry for nuclei with the same grade, and tested for statistical differences in these features. Experimental design and statistical analysis Thirty-nine nuclear digital image features of about 100 nuclei were measured in digital images of H&E stained slides of 81 breast biopsy specimens. One field with at least 5 ducts was evaluated for each patient. We compared features of nuclei with the same grade in multiple ducts of the same patient with ANOVA (or Welch test), and compared features of nuclei with the same grade in two ducts of different patients using 2-sided t-tests (P ≤ 0.05). Also, we compared image features for nuclei in patients with single grade to those with the same grade in patients with multiple grades using t-tests. Results Statistically significant differences were detected in nuclear features between ducts with the same nuclear grade, both in different ducts of the same patient, and between ducts in different patients with DCIS of more than one grade. Conclusion Nuclei in ducts visually described as having the same nuclear grade had significantly different subvisual digital image features. These subvisual differences may be considered additional manifestations of heterogeneity over and above differences that can be observed microscopically. This heterogeneity may explain the inconsistency of nuclear grading as a prognostic factor. PMID:20981137

Mutual information-based feature selection for radiomics

NASA Astrophysics Data System (ADS)

Oubel, Estanislao; Beaumont, Hubert; Iannessi, Antoine

2016-03-01

Background The extraction and analysis of image features (radiomics) is a promising field in the precision medicine era, with applications to prognosis, prediction, and response to treatment quantification. In this work, we present a mutual information - based method for quantifying reproducibility of features, a necessary step for qualification before their inclusion in big data systems. Materials and Methods Ten patients with Non-Small Cell Lung Cancer (NSCLC) lesions were followed over time (7 time points in average) with Computed Tomography (CT). Five observers segmented lesions by using a semi-automatic method and 27 features describing shape and intensity distribution were extracted. Inter-observer reproducibility was assessed by computing the multi-information (MI) of feature changes over time, and the variability of global extrema. Results The highest MI values were obtained for volume-based features (VBF). The lesion mass (M), surface to volume ratio (SVR) and volume (V) presented statistically significant higher values of MI than the rest of features. Within the same VBF group, SVR showed also the lowest variability of extrema. The correlation coefficient (CC) of feature values was unable to make a difference between features. Conclusions MI allowed to discriminate three features (M, SVR, and V) from the rest in a statistically significant manner. This result is consistent with the order obtained when sorting features by increasing values of extrema variability. MI is a promising alternative for selecting features to be considered as surrogate biomarkers in a precision medicine context.

Feature extraction and classification algorithms for high dimensional data

NASA Technical Reports Server (NTRS)

Lee, Chulhee; Landgrebe, David

1993-01-01

Feature extraction and classification algorithms for high dimensional data are investigated. Developments with regard to sensors for Earth observation are moving in the direction of providing much higher dimensional multispectral imagery than is now possible. In analyzing such high dimensional data, processing time becomes an important factor. With large increases in dimensionality and the number of classes, processing time will increase significantly. To address this problem, a multistage classification scheme is proposed which reduces the processing time substantially by eliminating unlikely classes from further consideration at each stage. Several truncation criteria are developed and the relationship between thresholds and the error caused by the truncation is investigated. Next an approach to feature extraction for classification is proposed based directly on the decision boundaries. It is shown that all the features needed for classification can be extracted from decision boundaries. A characteristic of the proposed method arises by noting that only a portion of the decision boundary is effective in discriminating between classes, and the concept of the effective decision boundary is introduced. The proposed feature extraction algorithm has several desirable properties: it predicts the minimum number of features necessary to achieve the same classification accuracy as in the original space for a given pattern recognition problem; and it finds the necessary feature vectors. The proposed algorithm does not deteriorate under the circumstances of equal means or equal covariances as some previous algorithms do. In addition, the decision boundary feature extraction algorithm can be used both for parametric and non-parametric classifiers. Finally, some problems encountered in analyzing high dimensional data are studied and possible solutions are proposed. First, the increased importance of the second order statistics in analyzing high dimensional data is recognized. By investigating the characteristics of high dimensional data, the reason why the second order statistics must be taken into account in high dimensional data is suggested. Recognizing the importance of the second order statistics, there is a need to represent the second order statistics. A method to visualize statistics using a color code is proposed. By representing statistics using color coding, one can easily extract and compare the first and the second statistics.

The Relationship between Dental Follicle Width and Maxillary Impacted Canines' Descriptive and Resorptive Features Using Cone-Beam Computed Tomography.

PubMed

Dağsuyu, İlhan Metin; Okşayan, Rıdvan; Kahraman, Fatih; Aydın, Mehmet; Bayrakdar, İbrahim Şevki; Uğurlu, Mehmet

2017-01-01

To assess the relationship between dental follicle width and maxillary impacted canines' descriptive and resorptive features with three-dimensional (3D) cone-beam computed tomography (CBCT). The study comprised 102 patients with cone-beam computed tomography 3D images and a total of 140 impacted canines. The association between maxillary impacted canine dental follicle width and the variables of gender, impaction side (right and left), localization of impacted canine (buccal, central, and palatal), and resorption of the adjacent laterals was compared. Measurements were analyzed with Student's t -test, Kruskal-Wallis test, and Mann-Whitney U statistical test. According to gender, no statistically significant differences were found in the follicle size of the maxillary impacted canine between males and females ( p > 0.05). Widths of the follicles were determined for the right and left impaction sides, and no statistically significant relation was found ( p > 0.05). There were statistically significant differences between root resorption degrees of lateral incisors and maxillary impacted canine follicle width ( p < 0.05). Statistically significant higher follicle width values were present in degree 2 (mild) resorption than in degree 1 (no) and degree 3 (moderate) resorption samples ( p < 0.05). No significant correlation was found between follicle width and the variables of gender, impaction side, and localization of maxillary impacted canines. Our study could not confirm that increased dental follicle width of the maxillary impacted canines exhibited more resorption risk for the adjacent lateral incisors.

Selecting relevant 3D image features of margin sharpness and texture for lung nodule retrieval.

PubMed

Ferreira, José Raniery; de Azevedo-Marques, Paulo Mazzoncini; Oliveira, Marcelo Costa

2017-03-01

Lung cancer is the leading cause of cancer-related deaths in the world. Its diagnosis is a challenge task to specialists due to several aspects on the classification of lung nodules. Therefore, it is important to integrate content-based image retrieval methods on the lung nodule classification process, since they are capable of retrieving similar cases from databases that were previously diagnosed. However, this mechanism depends on extracting relevant image features in order to obtain high efficiency. The goal of this paper is to perform the selection of 3D image features of margin sharpness and texture that can be relevant on the retrieval of similar cancerous and benign lung nodules. A total of 48 3D image attributes were extracted from the nodule volume. Border sharpness features were extracted from perpendicular lines drawn over the lesion boundary. Second-order texture features were extracted from a cooccurrence matrix. Relevant features were selected by a correlation-based method and a statistical significance analysis. Retrieval performance was assessed according to the nodule's potential malignancy on the 10 most similar cases and by the parameters of precision and recall. Statistical significant features reduced retrieval performance. Correlation-based method selected 2 margin sharpness attributes and 6 texture attributes and obtained higher precision compared to all 48 extracted features on similar nodule retrieval. Feature space dimensionality reduction of 83 % obtained higher retrieval performance and presented to be a computationaly low cost method of retrieving similar nodules for the diagnosis of lung cancer.

Feature-based classification of amino acid substitutions outside conserved functional protein domains.

PubMed

Gemovic, Branislava; Perovic, Vladimir; Glisic, Sanja; Veljkovic, Nevena

2013-01-01

There are more than 500 amino acid substitutions in each human genome, and bioinformatics tools irreplaceably contribute to determination of their functional effects. We have developed feature-based algorithm for the detection of mutations outside conserved functional domains (CFDs) and compared its classification efficacy with the most commonly used phylogeny-based tools, PolyPhen-2 and SIFT. The new algorithm is based on the informational spectrum method (ISM), a feature-based technique, and statistical analysis. Our dataset contained neutral polymorphisms and mutations associated with myeloid malignancies from epigenetic regulators ASXL1, DNMT3A, EZH2, and TET2. PolyPhen-2 and SIFT had significantly lower accuracies in predicting the effects of amino acid substitutions outside CFDs than expected, with especially low sensitivity. On the other hand, only ISM algorithm showed statistically significant classification of these sequences. It outperformed PolyPhen-2 and SIFT by 15% and 13%, respectively. These results suggest that feature-based methods, like ISM, are more suitable for the classification of amino acid substitutions outside CFDs than phylogeny-based tools.

Contrast-enhanced CT features of hepatoblastoma: Can we predict histopathology?

PubMed

Baheti, Akshay D; Luana Stanescu, A; Li, Ning; Chapman, Teresa

Hepatoblastoma is the most common hepatic malignancy occurring in the pediatric population. Intratumoral cellular behavior varies, and the small-cell undifferentiated histopathology carries a poorer prognosis than other tissue subtypes. Neoadjuvant chemotherapy is recommended for this tumor subtype prior to surgical resection in most cases. Early identification of tumors with poor prognosis could have a significant clinical impact. Objective The aim of this work was to identify imaging features of small-cell undifferentiated subtype hepatoblastoma that can help distinguish this subtype from more favorable tumors and potentially guide the clinical management. We also sought to characterize contrast-enhanced CT (CECT) features of hepatoblastoma that correlate with metastatic disease and patient outcome. Our study included 34 patients (24 males, 10 females) with a mean age of 16months (range: 0-46months) with surgically confirmed hepatoblastoma and available baseline abdominal imaging by CECT. Clinical data and CT abdominal images were retrospectively analyzed. Five tumors with small-cell undifferentiated components were identified. All of these tumors demonstrated irregular margins on CT imaging. Advanced PRETEXT stage, vascular invasion and irregular margins were associated with metastatic disease and decreased survival. Capsular retraction was also significantly associated with decreased survival. Irregular tumor margins demonstrated statistically significant association with the presence of small-cell undifferentiated components. No other imaging feature showed statistically significant association. Tumor margin irregularity, vascular invasion, capsular retraction, and PRETEXT stage correlate with worse patient outcomes. Irregular tumor margin was the only imaging feature significantly associated with more aggressive tumor subtype. Copyright © 2017 Elsevier Inc. All rights reserved.

Assessment and improvement of statistical tools for comparative proteomics analysis of sparse data sets with few experimental replicates.

PubMed

Schwämmle, Veit; León, Ileana Rodríguez; Jensen, Ole Nørregaard

2013-09-06

Large-scale quantitative analyses of biological systems are often performed with few replicate experiments, leading to multiple nonidentical data sets due to missing values. For example, mass spectrometry driven proteomics experiments are frequently performed with few biological or technical replicates due to sample-scarcity or due to duty-cycle or sensitivity constraints, or limited capacity of the available instrumentation, leading to incomplete results where detection of significant feature changes becomes a challenge. This problem is further exacerbated for the detection of significant changes on the peptide level, for example, in phospho-proteomics experiments. In order to assess the extent of this problem and the implications for large-scale proteome analysis, we investigated and optimized the performance of three statistical approaches by using simulated and experimental data sets with varying numbers of missing values. We applied three tools, including standard t test, moderated t test, also known as limma, and rank products for the detection of significantly changing features in simulated and experimental proteomics data sets with missing values. The rank product method was improved to work with data sets containing missing values. Extensive analysis of simulated and experimental data sets revealed that the performance of the statistical analysis tools depended on simple properties of the data sets. High-confidence results were obtained by using the limma and rank products methods for analyses of triplicate data sets that exhibited more than 1000 features and more than 50% missing values. The maximum number of differentially represented features was identified by using limma and rank products methods in a complementary manner. We therefore recommend combined usage of these methods as a novel and optimal way to detect significantly changing features in these data sets. This approach is suitable for large quantitative data sets from stable isotope labeling and mass spectrometry experiments and should be applicable to large data sets of any type. An R script that implements the improved rank products algorithm and the combined analysis is available.

Morphological texture assessment of oral bone as a screening tool for osteoporosis

NASA Astrophysics Data System (ADS)

Analoui, Mostafa; Eggertsson, Hafsteinn; Eckert, George

2001-07-01

Three classes of texture analysis approaches have been employed to assess the textural characteristic of oral bone. A set of linear structuring elements was used to compute granulometric features of trabecular bone. Multifractal analysis was also used to compute the fractal dimension of the corresponding tissues. In addition, some statistical features and histomorphometric parameters were computed. To assess the proposed approach we acquired digital intraoral radiographs of 47 subjects (14 males and 33 females). All radiographs were captured at 12 bits/pixel. Images were converted to binary form through a sliding locally adaptive thresholding approach. Each subject was scanned by DEXA for bone dosimetry. Subject were classified into one of the following three categories according World Health Organization (WHO) standard (1) healthy, (2) with osteopenia and (3) osteoporosis. In this study fractal dimension showed very low correlation with bone mineral density (BMD) measurements, which did not reach a level of statistical significance (p<0.5). However, entropy of pattern spectrum (EPS), along with statistical features and histomorphometric parameters, has shown correlation coefficients ranging from low to high, with statistical significance for both males and females. The results of this study indicate the utility of this approach for bone texture analysis. It is conjectured that designing a 2-D structuring element, specially tuned to trabecular bone texture, will increase the efficacy of the proposed method.

Time-frequency Features for Impedance Cardiography Signals During Anesthesia Using Different Distribution Kernels.

PubMed

Muñoz, Jesús Escrivá; Gambús, Pedro; Jensen, Erik W; Vallverdú, Montserrat

2018-01-01

This works investigates the time-frequency content of impedance cardiography signals during a propofol-remifentanil anesthesia. In the last years, impedance cardiography (ICG) is a technique which has gained much attention. However, ICG signals need further investigation. Time-Frequency Distributions (TFDs) with 5 different kernels are used in order to analyze impedance cardiography signals (ICG) before the start of the anesthesia and after the loss of consciousness. In total, ICG signals from one hundred and thirty-one consecutive patients undergoing major surgery under general anesthesia were analyzed. Several features were extracted from the calculated TFDs in order to characterize the time-frequency content of the ICG signals. Differences between those features before and after the loss of consciousness were studied. The Extended Modified Beta Distribution (EMBD) was the kernel for which most features shows statistically significant changes between before and after the loss of consciousness. Among all analyzed features, those based on entropy showed a sensibility, specificity and area under the curve of the receiver operating characteristic above 60%. The anesthetic state of the patient is reflected on linear and non-linear features extracted from the TFDs of the ICG signals. Especially, the EMBD is a suitable kernel for the analysis of ICG signals and offers a great range of features which change according to the patient's anesthesia state in a statistically significant way. Schattauer GmbH.

Quantitative Features of Liver Lesions, Lung Nodules, and Renal Stones at Multi-Detector Row CT Examinations: Dependency on Radiation Dose and Reconstruction Algorithm.

PubMed

Solomon, Justin; Mileto, Achille; Nelson, Rendon C; Roy Choudhury, Kingshuk; Samei, Ehsan

2016-04-01

To determine if radiation dose and reconstruction algorithm affect the computer-based extraction and analysis of quantitative imaging features in lung nodules, liver lesions, and renal stones at multi-detector row computed tomography (CT). Retrospective analysis of data from a prospective, multicenter, HIPAA-compliant, institutional review board-approved clinical trial was performed by extracting 23 quantitative imaging features (size, shape, attenuation, edge sharpness, pixel value distribution, and texture) of lesions on multi-detector row CT images of 20 adult patients (14 men, six women; mean age, 63 years; range, 38-72 years) referred for known or suspected focal liver lesions, lung nodules, or kidney stones. Data were acquired between September 2011 and April 2012. All multi-detector row CT scans were performed at two different radiation dose levels; images were reconstructed with filtered back projection, adaptive statistical iterative reconstruction, and model-based iterative reconstruction (MBIR) algorithms. A linear mixed-effects model was used to assess the effect of radiation dose and reconstruction algorithm on extracted features. Among the 23 imaging features assessed, radiation dose had a significant effect on five, three, and four of the features for liver lesions, lung nodules, and renal stones, respectively (P < .002 for all comparisons). Adaptive statistical iterative reconstruction had a significant effect on three, one, and one of the features for liver lesions, lung nodules, and renal stones, respectively (P < .002 for all comparisons). MBIR reconstruction had a significant effect on nine, 11, and 15 of the features for liver lesions, lung nodules, and renal stones, respectively (P < .002 for all comparisons). Of note, the measured size of lung nodules and renal stones with MBIR was significantly different than those for the other two algorithms (P < .002 for all comparisons). Although lesion texture was significantly affected by the reconstruction algorithm used (average of 3.33 features affected by MBIR throughout lesion types; P < .002, for all comparisons), no significant effect of the radiation dose setting was observed for all but one of the texture features (P = .002-.998). Radiation dose settings and reconstruction algorithms affect the extraction and analysis of quantitative imaging features in lesions at multi-detector row CT.

A Quantitative Features Analysis of Recommended No- and Low-Cost Preschool E-Books

ERIC Educational Resources Information Center

Parette, Howard P.; Blum, Craig; Luthin, Katie

2015-01-01

In recent years, recommended e-books have drawn increasing attention from early childhood education professionals. This study applied a quantitative descriptive features analysis of cost (n = 70) and no-cost (n = 60) e-books recommended by the Texas Computer Education Association. While t tests revealed no statistically significant differences…

Correlation of histogram analysis of apparent diffusion coefficient with uterine cervical pathologic finding.

PubMed

Lin, Yuning; Li, Hui; Chen, Ziqian; Ni, Ping; Zhong, Qun; Huang, Huijuan; Sandrasegaran, Kumar

2015-05-01

The purpose of this study was to investigate the application of histogram analysis of apparent diffusion coefficient (ADC) in characterizing pathologic features of cervical cancer and benign cervical lesions. This prospective study was approved by the institutional review board, and written informed consent was obtained. Seventy-three patients with cervical cancer (33-69 years old; 35 patients with International Federation of Gynecology and Obstetrics stage IB cervical cancer) and 38 patients (38-61 years old) with normal cervix or cervical benign lesions (control group) were enrolled. All patients underwent 3-T diffusion-weighted imaging (DWI) with b values of 0 and 800 s/mm(2). ADC values of the entire tumor in the patient group and the whole cervix volume in the control group were assessed. Mean ADC, median ADC, 25th and 75th percentiles of ADC, skewness, and kurtosis were calculated. Histogram parameters were compared between different pathologic features, as well as between stage IB cervical cancer and control groups. Mean ADC, median ADC, and 25th percentile of ADC were significantly higher for adenocarcinoma (p = 0.021, 0.006, and 0.004, respectively), and skewness was significantly higher for squamous cell carcinoma (p = 0.011). Median ADC was statistically significantly higher for well or moderately differentiated tumors (p = 0.044), and skewness was statistically significantly higher for poorly differentiated tumors (p = 0.004). No statistically significant difference of ADC histogram was observed between lymphovascular space invasion subgroups. All histogram parameters differed significantly between stage IB cervical cancer and control groups (p < 0.05). Distribution of ADCs characterized by histogram analysis may help to distinguish early-stage cervical cancer from normal cervix or cervical benign lesions and may be useful for evaluating the different pathologic features of cervical cancer.

Steganalysis based on reducing the differences of image statistical characteristics

NASA Astrophysics Data System (ADS)

Wang, Ran; Niu, Shaozhang; Ping, Xijian; Zhang, Tao

2018-04-01

Compared with the process of embedding, the image contents make a more significant impact on the differences of image statistical characteristics. This makes the image steganalysis to be a classification problem with bigger withinclass scatter distances and smaller between-class scatter distances. As a result, the steganalysis features will be inseparate caused by the differences of image statistical characteristics. In this paper, a new steganalysis framework which can reduce the differences of image statistical characteristics caused by various content and processing methods is proposed. The given images are segmented to several sub-images according to the texture complexity. Steganalysis features are separately extracted from each subset with the same or close texture complexity to build a classifier. The final steganalysis result is figured out through a weighted fusing process. The theoretical analysis and experimental results can demonstrate the validity of the framework.

Interactive classification and content-based retrieval of tissue images

NASA Astrophysics Data System (ADS)

Aksoy, Selim; Marchisio, Giovanni B.; Tusk, Carsten; Koperski, Krzysztof

2002-11-01

We describe a system for interactive classification and retrieval of microscopic tissue images. Our system models tissues in pixel, region and image levels. Pixel level features are generated using unsupervised clustering of color and texture values. Region level features include shape information and statistics of pixel level feature values. Image level features include statistics and spatial relationships of regions. To reduce the gap between low-level features and high-level expert knowledge, we define the concept of prototype regions. The system learns the prototype regions in an image collection using model-based clustering and density estimation. Different tissue types are modeled using spatial relationships of these regions. Spatial relationships are represented by fuzzy membership functions. The system automatically selects significant relationships from training data and builds models which can also be updated using user relevance feedback. A Bayesian framework is used to classify tissues based on these models. Preliminary experiments show that the spatial relationship models we developed provide a flexible and powerful framework for classification and retrieval of tissue images.

Statistical interpretation of machine learning-based feature importance scores for biomarker discovery.

PubMed

Huynh-Thu, Vân Anh; Saeys, Yvan; Wehenkel, Louis; Geurts, Pierre

2012-07-01

Univariate statistical tests are widely used for biomarker discovery in bioinformatics. These procedures are simple, fast and their output is easily interpretable by biologists but they can only identify variables that provide a significant amount of information in isolation from the other variables. As biological processes are expected to involve complex interactions between variables, univariate methods thus potentially miss some informative biomarkers. Variable relevance scores provided by machine learning techniques, however, are potentially able to highlight multivariate interacting effects, but unlike the p-values returned by univariate tests, these relevance scores are usually not statistically interpretable. This lack of interpretability hampers the determination of a relevance threshold for extracting a feature subset from the rankings and also prevents the wide adoption of these methods by practicians. We evaluated several, existing and novel, procedures that extract relevant features from rankings derived from machine learning approaches. These procedures replace the relevance scores with measures that can be interpreted in a statistical way, such as p-values, false discovery rates, or family wise error rates, for which it is easier to determine a significance level. Experiments were performed on several artificial problems as well as on real microarray datasets. Although the methods differ in terms of computing times and the tradeoff, they achieve in terms of false positives and false negatives, some of them greatly help in the extraction of truly relevant biomarkers and should thus be of great practical interest for biologists and physicians. As a side conclusion, our experiments also clearly highlight that using model performance as a criterion for feature selection is often counter-productive. Python source codes of all tested methods, as well as the MATLAB scripts used for data simulation, can be found in the Supplementary Material.

Statistical analysis of textural features for improved classification of oral histopathological images.

PubMed

Muthu Rama Krishnan, M; Shah, Pratik; Chakraborty, Chandan; Ray, Ajoy K

2012-04-01

The objective of this paper is to provide an improved technique, which can assist oncopathologists in correct screening of oral precancerous conditions specially oral submucous fibrosis (OSF) with significant accuracy on the basis of collagen fibres in the sub-epithelial connective tissue. The proposed scheme is composed of collagen fibres segmentation, its textural feature extraction and selection, screening perfomance enhancement under Gaussian transformation and finally classification. In this study, collagen fibres are segmented on R,G,B color channels using back-probagation neural network from 60 normal and 59 OSF histological images followed by histogram specification for reducing the stain intensity variation. Henceforth, textural features of collgen area are extracted using fractal approaches viz., differential box counting and brownian motion curve . Feature selection is done using Kullback-Leibler (KL) divergence criterion and the screening performance is evaluated based on various statistical tests to conform Gaussian nature. Here, the screening performance is enhanced under Gaussian transformation of the non-Gaussian features using hybrid distribution. Moreover, the routine screening is designed based on two statistical classifiers viz., Bayesian classification and support vector machines (SVM) to classify normal and OSF. It is observed that SVM with linear kernel function provides better classification accuracy (91.64%) as compared to Bayesian classifier. The addition of fractal features of collagen under Gaussian transformation improves Bayesian classifier's performance from 80.69% to 90.75%. Results are here studied and discussed.

Characterizing trabecular bone structure for assessing vertebral fracture risk on volumetric quantitative computed tomography

NASA Astrophysics Data System (ADS)

Nagarajan, Mahesh B.; Checefsky, Walter A.; Abidin, Anas Z.; Tsai, Halley; Wang, Xixi; Hobbs, Susan K.; Bauer, Jan S.; Baum, Thomas; Wismüller, Axel

2015-03-01

While the proximal femur is preferred for measuring bone mineral density (BMD) in fracture risk estimation, the introduction of volumetric quantitative computed tomography has revealed stronger associations between BMD and spinal fracture status. In this study, we propose to capture properties of trabecular bone structure in spinal vertebrae with advanced second-order statistical features for purposes of fracture risk assessment. For this purpose, axial multi-detector CT (MDCT) images were acquired from 28 spinal vertebrae specimens using a whole-body 256-row CT scanner with a dedicated calibration phantom. A semi-automated method was used to annotate the trabecular compartment in the central vertebral slice with a circular region of interest (ROI) to exclude cortical bone; pixels within were converted to values indicative of BMD. Six second-order statistical features derived from gray-level co-occurrence matrices (GLCM) and the mean BMD within the ROI were then extracted and used in conjunction with a generalized radial basis functions (GRBF) neural network to predict the failure load of the specimens; true failure load was measured through biomechanical testing. Prediction performance was evaluated with a root-mean-square error (RMSE) metric. The best prediction performance was observed with GLCM feature `correlation' (RMSE = 1.02 ± 0.18), which significantly outperformed all other GLCM features (p < 0.01). GLCM feature correlation also significantly outperformed MDCTmeasured mean BMD (RMSE = 1.11 ± 0.17) (p< 10-4). These results suggest that biomechanical strength prediction in spinal vertebrae can be significantly improved through characterization of trabecular bone structure with GLCM-derived texture features.

Cytopathology of non-invasive follicular thyroid neoplasm with papillary-like nuclear features: A comparative study with similar patterned papillary thyroid carcinoma variants.

PubMed

Mahajan, S; Agarwal, S; Kocheri, N; Jain, D; Mathur, S R; Iyer, V K

2018-06-01

Noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) is a recently described, indolent thyroid tumor, with well-defined histopathological diagnostic criteria. Cytology features are not well documented. We reviewed cytology of histologically proven cases of NIFTP and some of its common differentials to look for salient diagnostic features. Cases reported on histopathology as follicular variant of papillary thyroid carcinoma (FVPTC), or NIFTP between July 2015 and April 2017 having available cytology smears were retrieved and reclassified as NIFTP, FVPTC, and classical papillary thyroid carcinoma with predominant follicular pattern (PTC-FP). Cytological features were assessed, classified as per The Bethesda System for Reporting Cytopathology and compared. There were 23 NIFTP cases, 18 FVPTC and 8 PTC-FP. A microfollicle-predominant pattern was seen in all. Nuclear score was 2 in most NIFTP cases (61%). Pseudoinclusions were absent. NIFTP showed features of atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS) (III) in 61%, follicular neoplasm/suspicious for a follicular neoplasm (FN/SFN) (IV) in 35% and suspicious for malignancy (SFM) (V) in 4%. Most of the FVPTCs were also called FN/SFN (IV) (56%) or AUS/FLUS (III) (22%). Nuclear features did not statistically differ from NIFTP. PTC-FP showed high-grade cytology in 75%, and higher nuclear score (3 in 75%) in contrast to NIFTP (P = .003). NIFTP and FVPTC show a similar distribution among the Bethesda categories hence precluding conclusive distinction on cytology. PTC-FP, in contrast, was found to have a statistically significant higher nuclear score and more commonly showed malignant cytology. © 2018 John Wiley & Sons Ltd.

Understanding regulatory networks requires more than computing a multitude of graph statistics. Comment on "Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function" by O.C. Martin et al.

NASA Astrophysics Data System (ADS)

Tkačik, Gašper

2016-07-01

The article by O. Martin and colleagues provides a much needed systematic review of a body of work that relates the topological structure of genetic regulatory networks to evolutionary selection for function. This connection is very important. Using the current wealth of genomic data, statistical features of regulatory networks (e.g., degree distributions, motif composition, etc.) can be quantified rather easily; it is, however, often unclear how to interpret the results. On a graph theoretic level the statistical significance of the results can be evaluated by comparing observed graphs to ;randomized; ones (bravely ignoring the issue of how precisely to randomize!) and comparing the frequency of appearance of a particular network structure relative to a randomized null expectation. While this is a convenient operational test for statistical significance, its biological meaning is questionable. In contrast, an in-silico genotype-to-phenotype model makes explicit the assumptions about the network function, and thus clearly defines the expected network structures that can be compared to the case of no selection for function and, ultimately, to data.

Glue-Sniffing: A Comparison Study of Sniffers and Non-Sniffers.

ERIC Educational Resources Information Center

Jansen, P.; And Others

1992-01-01

Compared 22 glue sniffers and 22 nonsniffers from group of street children and adolescents living in supervised shelters. Found no statistically significant differences between groups on cognitive measures or biographical features. Shelter staff rated sniffers as significantly more disturbed in their relationships with others, although…

Dermatoglyphic features in patients with multiple sclerosis

PubMed Central

Sabanciogullari, Vedat; Cevik, Seyda; Karacan, Kezban; Bolayir, Ertugrul; Cimen, Mehmet

2014-01-01

Objective: To examine dermatoglyphic features to clarify implicated genetic predisposition in the etiology of multiple sclerosis (MS). Methods: The study was conducted between January and December 2013 in the Departments of Anatomy, and Neurology, Cumhuriyet University School of Medicine, Sivas, Turkey. The dermatoglyphic data of 61 patients, and a control group consisting of 62 healthy adults obtained with a digital scanner were transferred to a computer environment. The ImageJ program was used, and atd, dat, adt angles, a-b ridge count, sample types of all fingers, and ridge counts were calculated. Results: In both hands of the patients with MS, the a-b ridge count and ridge counts in all fingers increased, and the differences in these values were statistically significant. There was also a statistically significant increase in the dat angle in both hands of the MS patients. On the contrary, there was no statistically significant difference between the groups in terms of dermal ridge samples, and the most frequent sample in both groups was the ulnar loop. Conclusions: Aberrations in the distribution of dermatoglyphic samples support the genetic predisposition in MS etiology. Multiple sclerosis susceptible individuals may be determined by analyzing dermatoglyphic samples. PMID:25274586

Objects and categories: feature statistics and object processing in the ventral stream.

PubMed

Tyler, Lorraine K; Chiu, Shannon; Zhuang, Jie; Randall, Billi; Devereux, Barry J; Wright, Paul; Clarke, Alex; Taylor, Kirsten I

2013-10-01

Recognizing an object involves more than just visual analyses; its meaning must also be decoded. Extensive research has shown that processing the visual properties of objects relies on a hierarchically organized stream in ventral occipitotemporal cortex, with increasingly more complex visual features being coded from posterior to anterior sites culminating in the perirhinal cortex (PRC) in the anteromedial temporal lobe (aMTL). The neurobiological principles of the conceptual analysis of objects remain more controversial. Much research has focused on two neural regions-the fusiform gyrus and aMTL, both of which show semantic category differences, but of different types. fMRI studies show category differentiation in the fusiform gyrus, based on clusters of semantically similar objects, whereas category-specific deficits, specifically for living things, are associated with damage to the aMTL. These category-specific deficits for living things have been attributed to problems in differentiating between highly similar objects, a process that involves the PRC. To determine whether the PRC and the fusiform gyri contribute to different aspects of an object's meaning, with differentiation between confusable objects in the PRC and categorization based on object similarity in the fusiform, we carried out an fMRI study of object processing based on a feature-based model that characterizes the degree of semantic similarity and difference between objects and object categories. Participants saw 388 objects for which feature statistic information was available and named the objects at the basic level while undergoing fMRI scanning. After controlling for the effects of visual information, we found that feature statistics that capture similarity between objects formed category clusters in fusiform gyri, such that objects with many shared features (typical of living things) were associated with activity in the lateral fusiform gyri whereas objects with fewer shared features (typical of nonliving things) were associated with activity in the medial fusiform gyri. Significantly, a feature statistic reflecting differentiation between highly similar objects, enabling object-specific representations, was associated with bilateral PRC activity. These results confirm that the statistical characteristics of conceptual object features are coded in the ventral stream, supporting a conceptual feature-based hierarchy, and integrating disparate findings of category responses in fusiform gyri and category deficits in aMTL into a unifying neurocognitive framework.

Joint resonant CMB power spectrum and bispectrum estimation

NASA Astrophysics Data System (ADS)

Meerburg, P. Daniel; Münchmeyer, Moritz; Wandelt, Benjamin

2016-02-01

We develop the tools necessary to assess the statistical significance of resonant features in the CMB correlation functions, combining power spectrum and bispectrum measurements. This significance is typically addressed by running a large number of simulations to derive the probability density function (PDF) of the feature-amplitude in the Gaussian case. Although these simulations are tractable for the power spectrum, for the bispectrum they require significant computational resources. We show that, by assuming that the PDF is given by a multivariate Gaussian where the covariance is determined by the Fisher matrix of the sine and cosine terms, we can efficiently produce spectra that are statistically close to those derived from full simulations. By drawing a large number of spectra from this PDF, both for the power spectrum and the bispectrum, we can quickly determine the statistical significance of candidate signatures in the CMB, considering both single frequency and multifrequency estimators. We show that for resonance models, cosmology and foreground parameters have little influence on the estimated amplitude, which allows us to simplify the analysis considerably. A more precise likelihood treatment can then be applied to candidate signatures only. We also discuss a modal expansion approach for the power spectrum, aimed at quickly scanning through large families of oscillating models.

TU-D-207B-03: Early Assessment of Response to Chemoradiotherapy Based On Textural Analysis of Pre and Mid-Treatment FDG-PET Image in Locally Advanced Head and Neck Cancer

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

Cui, Y; Pollom, E; Loo, B

Purpose: To evaluate whether tumor textural features extracted from both pre- and mid-treatment FDG-PET images predict early response to chemoradiotherapy in locally advanced head and neck cancer, and investigate whether they provide complementary value to conventional volume-based measurements. Methods: Ninety-four patients with locally advanced head and neck cancers were retrospectively studied. All patients received definitive chemoradiotherapy and underwent FDG-PET planning scans both before and during treatment. Within the primary tumor we extracted 6 textural features based on gray-level co-occurrence matrices (GLCM): entropy, dissimilarity, contrast, correlation, energy, and homogeneity. These image features were evaluated for their predictive power of treatment responsemore » to chemoradiotherapy in terms of local recurrence free survival (LRFS) and progression free survival (PFS). Logrank test were used to assess the statistical significance of the stratification between low- and high-risk groups. P-values were adjusted for multiple comparisons by the false discovery rate (FDR) method. Results: All six textural features extracted from pre-treatment PET images significantly differentiated low- and high-risk patient groups for LRFS (P=0.011–0.038) and PFS (P=0.029–0.034). On the other hand, none of the textural features on mid-treatment PET images was statistically significant in stratifying LRFS (P=0.212–0.445) or PFS (P=0.168–0.299). An imaging signature that combines textural feature (GLCM homogeneity) and metabolic tumor volume showed an improved performance for predicting LRFS (hazard ratio: 22.8, P<0.0001) and PFS (hazard ratio: 13.9, P=0.0005) in leave-one-out cross validation. Intra-tumor heterogeneity measured by textural features was significantly lower in mid-treatment PET images than in pre-treatment PET images (T-test: P<1.4e-6). Conclusion: Tumor textural features on pretreatment FDG-PET images are predictive for response to chemoradiotherapy in locally advanced head and neck cancer. The complementary information offered by textural features improves patient stratification and may potentially aid in personalized risk-adaptive therapy.« less

Facial anthropometric differences among gender, ethnicity, and age groups.

PubMed

Zhuang, Ziqing; Landsittel, Douglas; Benson, Stacey; Roberge, Raymond; Shaffer, Ronald

2010-06-01

The impact of race/ethnicity upon facial anthropometric data in the US workforce, on the development of personal protective equipment, has not been investigated to any significant degree. The proliferation of minority populations in the US workforce has increased the need to investigate differences in facial dimensions among these workers. The objective of this study was to determine the face shape and size differences among race and age groups from the National Institute for Occupational Safety and Health survey of 3997 US civilian workers. Survey participants were divided into two gender groups, four racial/ethnic groups, and three age groups. Measurements of height, weight, neck circumference, and 18 facial dimensions were collected using traditional anthropometric techniques. A multivariate analysis of the data was performed using Principal Component Analysis. An exploratory analysis to determine the effect of different demographic factors had on anthropometric features was assessed via a linear model. The 21 anthropometric measurements, body mass index, and the first and second principal component scores were dependent variables, while gender, ethnicity, age, occupation, weight, and height served as independent variables. Gender significantly contributes to size for 19 of 24 dependent variables. African-Americans have statistically shorter, wider, and shallower noses than Caucasians. Hispanic workers have 14 facial features that are significantly larger than Caucasians, while their nose protrusion, height, and head length are significantly shorter. The other ethnic group was composed primarily of Asian subjects and has statistically different dimensions from Caucasians for 16 anthropometric values. Nineteen anthropometric values for subjects at least 45 years of age are statistically different from those measured for subjects between 18 and 29 years of age. Workers employed in manufacturing, fire fighting, healthcare, law enforcement, and other occupational groups have facial features that differ significantly than those in construction. Statistically significant differences in facial anthropometric dimensions (P < 0.05) were noted between males and females, all racial/ethnic groups, and the subjects who were at least 45 years old when compared to workers between 18 and 29 years of age. These findings could be important to the design and manufacture of respirators, as well as employers responsible for supplying respiratory protective equipment to their employees.

Characterizing chaotic melodies in automatic music composition

NASA Astrophysics Data System (ADS)

Coca, Andrés E.; Tost, Gerard O.; Zhao, Liang

2010-09-01

In this paper, we initially present an algorithm for automatic composition of melodies using chaotic dynamical systems. Afterward, we characterize chaotic music in a comprehensive way as comprising three perspectives: musical discrimination, dynamical influence on musical features, and musical perception. With respect to the first perspective, the coherence between generated chaotic melodies (continuous as well as discrete chaotic melodies) and a set of classical reference melodies is characterized by statistical descriptors and melodic measures. The significant differences among the three types of melodies are determined by discriminant analysis. Regarding the second perspective, the influence of dynamical features of chaotic attractors, e.g., Lyapunov exponent, Hurst coefficient, and correlation dimension, on melodic features is determined by canonical correlation analysis. The last perspective is related to perception of originality, complexity, and degree of melodiousness (Euler's gradus suavitatis) of chaotic and classical melodies by nonparametric statistical tests.

Usefulness of current international air transport statistics

DOT National Transportation Integrated Search

1999-05-01

International air transportation is the fastest growing segment of transportation. It performs a major function in the globalization process and is a significant feature of the late 20th century. Public policy regarding international air transportati...

A comparative analysis of user preference-based and existing knowledge management systems attributes in the aerospace industry

NASA Astrophysics Data System (ADS)

Varghese, Nishad G.

Knowledge management (KM) exists in various forms throughout organizations. Process documentation, training courses, and experience sharing are examples of KM activities performed daily. The goal of KM systems (KMS) is to provide a tool set which serves to standardize the creation, sharing, and acquisition of business critical information. Existing literature provides numerous examples of targeted evaluations of KMS, focusing on specific system attributes. This research serves to bridge the targeted evaluations with an industry-specific, holistic approach. The user preferences of aerospace employees in engineering and engineering-related fields were compared to profiles of existing aerospace KMS based on three attribute categories: technical features, system administration, and user experience. The results indicated there is a statistically significant difference between aerospace user preferences and existing profiles in the user experience attribute category, but no statistically significant difference in the technical features and system administration attribute categories. Additional analysis indicated in-house developed systems exhibit higher technical features and user experience ratings than commercial-off-the-self (COTS) systems.

Investigating the cognitive structure of stereotypes: Generic beliefs about groups predict social judgments better than statistical beliefs.

PubMed

Hammond, Matthew D; Cimpian, Andrei

2017-05-01

Stereotypes are typically defined as beliefs about groups, but this definition is underspecified. Beliefs about groups can be generic or statistical. Generic beliefs attribute features to entire groups (e.g., men are strong), whereas statistical beliefs encode the perceived prevalence of features (e.g., how common it is for men to be strong). In the present research, we sought to determine which beliefs-generic or statistical-are more central to the cognitive structure of stereotypes. Specifically, we tested whether generic or statistical beliefs are more influential in people's social judgments, on the assumption that greater functional importance indicates greater centrality in stereotype structure. Relative to statistical beliefs, generic beliefs about social groups were significantly stronger predictors of expectations (Studies 1-3) and explanations (Study 4) for unfamiliar individuals' traits. In addition, consistent with prior evidence that generic beliefs are cognitively simpler than statistical beliefs, generic beliefs were particularly predictive of social judgments for participants with more intuitive (vs. analytic) cognitive styles and for participants higher (vs. lower) in authoritarianism, who tend to view outgroups in simplistic, all-or-none terms. The present studies suggest that generic beliefs about groups are more central than statistical beliefs to the cognitive structure of stereotypes. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

PROBING FOR EVIDENCE OF PLUMES ON EUROPA WITH HST /STIS

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

Sparks, W. B.; Bergeron, E.; Cracraft, M.

2016-10-01

Roth et al. (2014a) reported evidence for plumes of water venting from a southern high latitude region on Europa: spectroscopic detection of off-limb line emission from the dissociation products of water. Here, we present Hubble Space Telescope direct images of Europa in the far-ultraviolet (FUV) as it transited the smooth face of Jupiter to measure absorption from gas or aerosols beyond the Europa limb. Out of 10 observations, we found 3 in which plume activity could be implicated. Two observations showed statistically significant features at latitudes similar to Roth et al., and the third at a more equatorial location. Wemore » consider potential systematic effects that might influence the statistical analysis and create artifacts, and are unable to find any that can definitively explain the features, although there are reasons to be cautious. If the apparent absorption features are real, the magnitude of implied outgassing is similar to that of the Roth et al. feature; however, the apparent activity appears more frequently in our data.« less

Automated breast tissue density assessment using high order regional texture descriptors in mammography

NASA Astrophysics Data System (ADS)

Law, Yan Nei; Lieng, Monica Keiko; Li, Jingmei; Khoo, David Aik-Aun

2014-03-01

Breast cancer is the most common cancer and second leading cause of cancer death among women in the US. The relative survival rate is lower among women with a more advanced stage at diagnosis. Early detection through screening is vital. Mammography is the most widely used and only proven screening method for reliably and effectively detecting abnormal breast tissues. In particular, mammographic density is one of the strongest breast cancer risk factors, after age and gender, and can be used to assess the future risk of disease before individuals become symptomatic. A reliable method for automatic density assessment would be beneficial and could assist radiologists in the evaluation of mammograms. To address this problem, we propose a density classification method which uses statistical features from different parts of the breast. Our method is composed of three parts: breast region identification, feature extraction and building ensemble classifiers for density assessment. It explores the potential of the features extracted from second and higher order statistical information for mammographic density classification. We further investigate the registration of bilateral pairs and time-series of mammograms. The experimental results on 322 mammograms demonstrate that (1) a classifier using features from dense regions has higher discriminative power than a classifier using only features from the whole breast region; (2) these high-order features can be effectively combined to boost the classification accuracy; (3) a classifier using these statistical features from dense regions achieves 75% accuracy, which is a significant improvement from 70% accuracy obtained by the existing approaches.

Max-AUC Feature Selection in Computer-Aided Detection of Polyps in CT Colonography

PubMed Central

Xu, Jian-Wu; Suzuki, Kenji

2014-01-01

We propose a feature selection method based on a sequential forward floating selection (SFFS) procedure to improve the performance of a classifier in computerized detection of polyps in CT colonography (CTC). The feature selection method is coupled with a nonlinear support vector machine (SVM) classifier. Unlike the conventional linear method based on Wilks' lambda, the proposed method selected the most relevant features that would maximize the area under the receiver operating characteristic curve (AUC), which directly maximizes classification performance, evaluated based on AUC value, in the computer-aided detection (CADe) scheme. We presented two variants of the proposed method with different stopping criteria used in the SFFS procedure. The first variant searched all feature combinations allowed in the SFFS procedure and selected the subsets that maximize the AUC values. The second variant performed a statistical test at each step during the SFFS procedure, and it was terminated if the increase in the AUC value was not statistically significant. The advantage of the second variant is its lower computational cost. To test the performance of the proposed method, we compared it against the popular stepwise feature selection method based on Wilks' lambda for a colonic-polyp database (25 polyps and 2624 nonpolyps). We extracted 75 morphologic, gray-level-based, and texture features from the segmented lesion candidate regions. The two variants of the proposed feature selection method chose 29 and 7 features, respectively. Two SVM classifiers trained with these selected features yielded a 96% by-polyp sensitivity at false-positive (FP) rates of 4.1 and 6.5 per patient, respectively. Experiments showed a significant improvement in the performance of the classifier with the proposed feature selection method over that with the popular stepwise feature selection based on Wilks' lambda that yielded 18.0 FPs per patient at the same sensitivity level. PMID:24608058

Max-AUC feature selection in computer-aided detection of polyps in CT colonography.

PubMed

Xu, Jian-Wu; Suzuki, Kenji

2014-03-01

We propose a feature selection method based on a sequential forward floating selection (SFFS) procedure to improve the performance of a classifier in computerized detection of polyps in CT colonography (CTC). The feature selection method is coupled with a nonlinear support vector machine (SVM) classifier. Unlike the conventional linear method based on Wilks' lambda, the proposed method selected the most relevant features that would maximize the area under the receiver operating characteristic curve (AUC), which directly maximizes classification performance, evaluated based on AUC value, in the computer-aided detection (CADe) scheme. We presented two variants of the proposed method with different stopping criteria used in the SFFS procedure. The first variant searched all feature combinations allowed in the SFFS procedure and selected the subsets that maximize the AUC values. The second variant performed a statistical test at each step during the SFFS procedure, and it was terminated if the increase in the AUC value was not statistically significant. The advantage of the second variant is its lower computational cost. To test the performance of the proposed method, we compared it against the popular stepwise feature selection method based on Wilks' lambda for a colonic-polyp database (25 polyps and 2624 nonpolyps). We extracted 75 morphologic, gray-level-based, and texture features from the segmented lesion candidate regions. The two variants of the proposed feature selection method chose 29 and 7 features, respectively. Two SVM classifiers trained with these selected features yielded a 96% by-polyp sensitivity at false-positive (FP) rates of 4.1 and 6.5 per patient, respectively. Experiments showed a significant improvement in the performance of the classifier with the proposed feature selection method over that with the popular stepwise feature selection based on Wilks' lambda that yielded 18.0 FPs per patient at the same sensitivity level.

Three-dimensional textural features of conventional MRI improve diagnostic classification of childhood brain tumours.

PubMed

Fetit, Ahmed E; Novak, Jan; Peet, Andrew C; Arvanitits, Theodoros N

2015-09-01

The aim of this study was to assess the efficacy of three-dimensional texture analysis (3D TA) of conventional MR images for the classification of childhood brain tumours in a quantitative manner. The dataset comprised pre-contrast T1 - and T2-weighted MRI series obtained from 48 children diagnosed with brain tumours (medulloblastoma, pilocytic astrocytoma and ependymoma). 3D and 2D TA were carried out on the images using first-, second- and higher order statistical methods. Six supervised classification algorithms were trained with the most influential 3D and 2D textural features, and their performances in the classification of tumour types, using the two feature sets, were compared. Model validation was carried out using the leave-one-out cross-validation (LOOCV) approach, as well as stratified 10-fold cross-validation, in order to provide additional reassurance. McNemar's test was used to test the statistical significance of any improvements demonstrated by 3D-trained classifiers. Supervised learning models trained with 3D textural features showed improved classification performances to those trained with conventional 2D features. For instance, a neural network classifier showed 12% improvement in area under the receiver operator characteristics curve (AUC) and 19% in overall classification accuracy. These improvements were statistically significant for four of the tested classifiers, as per McNemar's tests. This study shows that 3D textural features extracted from conventional T1 - and T2-weighted images can improve the diagnostic classification of childhood brain tumours. Long-term benefits of accurate, yet non-invasive, diagnostic aids include a reduction in surgical procedures, improvement in surgical and therapy planning, and support of discussions with patients' families. It remains necessary, however, to extend the analysis to a multicentre cohort in order to assess the scalability of the techniques used. Copyright © 2015 John Wiley & Sons, Ltd.

Fukunaga-Koontz feature transformation for statistical structural damage detection and hierarchical neuro-fuzzy damage localisation

NASA Astrophysics Data System (ADS)

Hoell, Simon; Omenzetter, Piotr

2017-07-01

Considering jointly damage sensitive features (DSFs) of signals recorded by multiple sensors, applying advanced transformations to these DSFs and assessing systematically their contribution to damage detectability and localisation can significantly enhance the performance of structural health monitoring systems. This philosophy is explored here for partial autocorrelation coefficients (PACCs) of acceleration responses. They are interrogated with the help of the linear discriminant analysis based on the Fukunaga-Koontz transformation using datasets of the healthy and selected reference damage states. Then, a simple but efficient fast forward selection procedure is applied to rank the DSF components with respect to statistical distance measures specialised for either damage detection or localisation. For the damage detection task, the optimal feature subsets are identified based on the statistical hypothesis testing. For damage localisation, a hierarchical neuro-fuzzy tool is developed that uses the DSF ranking to establish its own optimal architecture. The proposed approaches are evaluated experimentally on data from non-destructively simulated damage in a laboratory scale wind turbine blade. The results support our claim of being able to enhance damage detectability and localisation performance by transforming and optimally selecting DSFs. It is demonstrated that the optimally selected PACCs from multiple sensors or their Fukunaga-Koontz transformed versions can not only improve the detectability of damage via statistical hypothesis testing but also increase the accuracy of damage localisation when used as inputs into a hierarchical neuro-fuzzy network. Furthermore, the computational effort of employing these advanced soft computing models for damage localisation can be significantly reduced by using transformed DSFs.

[Fixed appliance therapy in patients with impaired short-circuit in the anterior part of the maxilla].

PubMed

Matthews-Brzozowska, Teresa; Pobol-Aidi, Małgorzata; Cudziło, Dorota

2015-03-01

Malocclusion in the anterior segment of maxilla and mandible are easily visible not only for dentists but also for the doctors of other specialties. Early diagnosis and appropriate therapy is important not only for occlusion but also for aesthetic reasons. The aim of the paper is to evaluate the anterior segment of maxilla and mandible in patients with malocclusion in this part and correct occlusion in the lateral segments. Medical documentation, i.e. medical history, extra- and intraoral radiograms, diagnostic casts, panoramic and lateral cephalometric radiograms of patients aged 7-12 diagnosed with malocclusion in the anterior segment of maxilla and mandible and who were treated with a fixed sectional appliance and facemask was analyzed. Descriptive and cephalometric features were analyzed before (T1) and after (T2) the treatment in 25 children. The differences between the status before and after the treatment, and the extent of change between T1 and T2 were analyzed. Statistical analysis of mean values of selected metrical features before (at T1) and after (at T2) the treatment has revealed that all metrical features concerning soft, bony and dental tissues determining the facial profile, the shape of the bony and dental structures have changed and have reached values which are closer to the norm for the population for selected features. The changes were statistically significant (p<0.0001). Treatment with fixed appliances segment facemask resulted in statistically significant improvement in the parameters investigated, which demonstrates the applicability of this therapy in the treatment of anterior maxillary segment in patients with mixed dentition. © 2015 MEDPRESS.

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.

Reproducibility and Prognosis of Quantitative Features Extracted from CT Images12

PubMed Central

Balagurunathan, Yoganand; Gu, Yuhua; Wang, Hua; Kumar, Virendra; Grove, Olya; Hawkins, Sam; Kim, Jongphil; Goldgof, Dmitry B; Hall, Lawrence O; Gatenby, Robert A; Gillies, Robert J

2014-01-01

We study the reproducibility of quantitative imaging features that are used to describe tumor shape, size, and texture from computed tomography (CT) scans of non-small cell lung cancer (NSCLC). CT images are dependent on various scanning factors. We focus on characterizing image features that are reproducible in the presence of variations due to patient factors and segmentation methods. Thirty-two NSCLC nonenhanced lung CT scans were obtained from the Reference Image Database to Evaluate Response data set. The tumors were segmented using both manual (radiologist expert) and ensemble (software-automated) methods. A set of features (219 three-dimensional and 110 two-dimensional) was computed, and quantitative image features were statistically filtered to identify a subset of reproducible and nonredundant features. The variability in the repeated experiment was measured by the test-retest concordance correlation coefficient (CCCTreT). The natural range in the features, normalized to variance, was measured by the dynamic range (DR). In this study, there were 29 features across segmentation methods found with CCCTreT and DR ≥ 0.9 and R2Bet ≥ 0.95. These reproducible features were tested for predicting radiologist prognostic score; some texture features (run-length and Laws kernels) had an area under the curve of 0.9. The representative features were tested for their prognostic capabilities using an independent NSCLC data set (59 lung adenocarcinomas), where one of the texture features, run-length gray-level nonuniformity, was statistically significant in separating the samples into survival groups (P ≤ .046). PMID:24772210

IDH mutation assessment of glioma using texture features of multimodal MR images

NASA Astrophysics Data System (ADS)

Zhang, Xi; Tian, Qiang; Wu, Yu-Xia; Xu, Xiao-Pan; Li, Bao-Juan; Liu, Yi-Xiong; Liu, Yang; Lu, Hong-Bing

2017-03-01

Purpose: To 1) find effective texture features from multimodal MRI that can distinguish IDH mutant and wild status, and 2) propose a radiomic strategy for preoperatively detecting IDH mutation patients with glioma. Materials and Methods: 152 patients with glioma were retrospectively included from the Cancer Genome Atlas. Corresponding T1-weighted image before- and post-contrast, T2-weighted image and fluid-attenuation inversion recovery image from the Cancer Imaging Archive were analyzed. Specific statistical tests were applied to analyze the different kind of baseline information of LrGG patients. Finally, 168 texture features were derived from multimodal MRI per patient. Then the support vector machine-based recursive feature elimination (SVM-RFE) and classification strategy was adopted to find the optimal feature subset and build the identification models for detecting the IDH mutation. Results: Among 152 patients, 92 and 60 were confirmed to be IDH-wild and mutant, respectively. Statistical analysis showed that the patients without IDH mutation was significant older than patients with IDH mutation (p<0.01), and the distribution of some histological subtypes was significant different between IDH wild and mutant groups (p<0.01). After SVM-RFE, 15 optimal features were determined for IDH mutation detection. The accuracy, sensitivity, specificity, and AUC after SVM-RFE and parameter optimization were 82.2%, 85.0%, 78.3%, and 0.841, respectively. Conclusion: This study presented a radiomic strategy for noninvasively discriminating IDH mutation of patients with glioma. It effectively incorporated kinds of texture features from multimodal MRI, and SVM-based classification strategy. Results suggested that features selected from SVM-RFE were more potential to identifying IDH mutation. The proposed radiomics strategy could facilitate the clinical decision making in patients with glioma.

A hierarchical fuzzy rule-based approach to aphasia diagnosis.

PubMed

Akbarzadeh-T, Mohammad-R; Moshtagh-Khorasani, Majid

2007-10-01

Aphasia diagnosis is a particularly challenging medical diagnostic task due to the linguistic uncertainty and vagueness, inconsistencies in the definition of aphasic syndromes, large number of measurements with imprecision, natural diversity and subjectivity in test objects as well as in opinions of experts who diagnose the disease. To efficiently address this diagnostic process, a hierarchical fuzzy rule-based structure is proposed here that considers the effect of different features of aphasia by statistical analysis in its construction. This approach can be efficient for diagnosis of aphasia and possibly other medical diagnostic applications due to its fuzzy and hierarchical reasoning construction. Initially, the symptoms of the disease which each consists of different features are analyzed statistically. The measured statistical parameters from the training set are then used to define membership functions and the fuzzy rules. The resulting two-layered fuzzy rule-based system is then compared with a back propagating feed-forward neural network for diagnosis of four Aphasia types: Anomic, Broca, Global and Wernicke. In order to reduce the number of required inputs, the technique is applied and compared on both comprehensive and spontaneous speech tests. Statistical t-test analysis confirms that the proposed approach uses fewer Aphasia features while also presenting a significant improvement in terms of accuracy.

[Lymphocytic infiltration in uveal melanoma].

PubMed

Sach, J; Kocur, J

1993-11-01

After our observation of lymphocytic infiltration in uveal melanomas we present theoretical review to this interesting topic. Due to relatively low incidence of this feature we haven't got sufficiently large collection of cases for presentation of our statistically significant conclusions.

Random fractional ultrapulsed CO2 resurfacing of photodamaged facial skin: long-term evaluation.

PubMed

Tretti Clementoni, Matteo; Galimberti, Michela; Tourlaki, Athanasia; Catenacci, Maximilian; Lavagno, Rosalia; Bencini, Pier Luca

2013-02-01

Although numerous papers have recently been published on ablative fractional resurfacing, there is a lack of information in literature on very long-term results. The aim of this retrospective study is to evaluate the efficacy, adverse side effects, and long-term results of a random fractional ultrapulsed CO2 laser on a large population with photodamaged facial skin. Three hundred twelve patients with facial photodamaged skin were enrolled and underwent a single full-face treatment. Six aspects of photodamaged skin were recorded using a 5 point scale at 3, 6, and 24 months after the treatment. The results were compared with a non-parametric statistical test, the Wilcoxon's exact test. Three hundred one patients completed the study. All analyzed features showed a significant statistical improvement 3 months after the procedure. Three months later all features, except for pigmentations, once again showed a significant statistical improvement. Results after 24 months were similar to those assessed 18 months before. No long-term or other serious complications were observed. From the significant number of patients analyzed, long-term results demonstrate not only how fractional ultrapulsed CO2 resurfacing can achieve good results on photodamaged facial skin but also how these results can be considered stable 2 years after the procedure.

Evaluating the statistical performance of less applied algorithms in classification of worldview-3 imagery data in an urbanized landscape

NASA Astrophysics Data System (ADS)

Ranaie, Mehrdad; Soffianian, Alireza; Pourmanafi, Saeid; Mirghaffari, Noorollah; Tarkesh, Mostafa

2018-03-01

In recent decade, analyzing the remotely sensed imagery is considered as one of the most common and widely used procedures in the environmental studies. In this case, supervised image classification techniques play a central role. Hence, taking a high resolution Worldview-3 over a mixed urbanized landscape in Iran, three less applied image classification methods including Bagged CART, Stochastic gradient boosting model and Neural network with feature extraction were tested and compared with two prevalent methods: random forest and support vector machine with linear kernel. To do so, each method was run ten time and three validation techniques was used to estimate the accuracy statistics consist of cross validation, independent validation and validation with total of train data. Moreover, using ANOVA and Tukey test, statistical difference significance between the classification methods was significantly surveyed. In general, the results showed that random forest with marginal difference compared to Bagged CART and stochastic gradient boosting model is the best performing method whilst based on independent validation there was no significant difference between the performances of classification methods. It should be finally noted that neural network with feature extraction and linear support vector machine had better processing speed than other.

Differential Expression and Clinical Significance of DNA Methyltransferase 3B (DNMT3B), Phosphatase and Tensin Homolog (PTEN) and Human MutL Homologs 1 (hMLH1) in Endometrial Carcinomas.

PubMed

Li, Wenting; Wang, Ying; Fang, Xinzhi; Zhou, Mei; Li, Yiqun; Dong, Ying; Wang, Ruozheng

2017-02-21

BACKGROUND The aim of this study was to investigate the expression and the clinicopathologic significance of DNA methyltransferase 3B (DNMT3B), phosphatase and tensin homolog (PTEN) and human MutL homologs 1 (hMLH1) in endometrial carcinomas between Han and Uygur women in Xinjiang. MATERIAL AND METHODS The expression of DNMT3B, PTEN, and hMLH1 in endometrial carcinomas were assessed by immunohistochemistry, followed by an analysis of their relationship to clinical-pathological features and prognosis. RESULTS There were a 61.7% (95/154) overexpression of DNMT3B, 50.0% (77/154) loss of PTEN expression and 18.2% (28/154) loss of hMLH1 expression. The expression of DNMT3B and PTEN in endometrial carcinomas was statistically significantly different between Uygur women and Han women (p=0.001, p=0.010, respectively). DNMT3B expression was statistically significant based on the grade of endometrial carcinomas (p=0.031). PTEN loss was statistically significant between endometrioid carcinomas (ECs) and non endometrioid carcinomas (NECs) (p=0.040). DNMT3B expression was statistically significant in different myometrial invasion groups in Uygur women (p=0.010). Furthermore, the correlation of DNMT3B and PTEN expression was significant in endometrial carcinomas (p=0.021). PTEN expression was statistically significant in the overall survival (OS) rate of women with endometrial cancers (p=0.041). CONCLUSIONS Our findings suggest that PTEN and DNMT3B possess common regulation features as well as certain ethnic differences in expression between Han women and Uygur women. An interaction may exist in the pathogenesis of endometrial carcinoma. DNMT3B was expressed differently in cases of myometrial invasion and PTEN was associated with OS, which suggested that these molecular markers may be useful in the evaluation of the biological behavior of endometrial carcinomas and may be useful indicators of prognosis in women with endometrial carcinomas.

Differential Expression and Clinical Significance of DNA Methyltransferase 3B (DNMT3B), Phosphatase and Tensin Homolog (PTEN) and Human MutL Homologs 1 (hMLH1) in Endometrial Carcinomas

PubMed Central

Li, Wenting; Wang, Ying; Fang, Xinzhi; Zhou, Mei; Li, Yiqun; Dong, Ying; Wang, Ruozheng

2017-01-01

Background The aim of this study was to investigate the expression and the clinicopathologic significance of DNA methyltransferase 3B (DNMT3B), phosphatase and tensin homolog (PTEN) and human MutL homologs 1 (hMLH1) in endometrial carcinomas between Han and Uygur women in Xinjiang. Material/Methods The expression of DNMT3B, PTEN, and hMLH1 in endometrial carcinomas were assessed by immunohistochemistry, followed by an analysis of their relationship to clinical-pathological features and prognosis. Results There were a 61.7% (95/154) overexpression of DNMT3B, 50.0% (77/154) loss of PTEN expression and 18.2% (28/154) loss of hMLH1 expression. The expression of DNMT3B and PTEN in endometrial carcinomas was statistically significantly different between Uygur women and Han women (p=0.001, p=0.010, respectively). DNMT3B expression was statistically significant based on the grade of endometrial carcinomas (p=0.031). PTEN loss was statistically significant between endometrioid carcinomas (ECs) and non endometrioid carcinomas (NECs) (p=0.040). DNMT3B expression was statistically significant in different myometrial invasion groups in Uygur women (p=0.010). Furthermore, the correlation of DNMT3B and PTEN expression was significant in endometrial carcinomas (p=0.021). PTEN expression was statistically significant in the overall survival (OS) rate of women with endometrial cancers (p=0.041). Conclusions Our findings suggest that PTEN and DNMT3B possess common regulation features as well as certain ethnic differences in expression between Han women and Uygur women. An interaction may exist in the pathogenesis of endometrial carcinoma. DNMT3B was expressed differently in cases of myometrial invasion and PTEN was associated with OS, which suggested that these molecular markers may be useful in the evaluation of the biological behavior of endometrial carcinomas and may be useful indicators of prognosis in women with endometrial carcinomas. PMID:28220037

Assessing Statistically Significant Heavy-Metal Concentrations in Abandoned Mine Areas via Hot Spot Analysis of Portable XRF Data

PubMed Central

Kim, Sung-Min; Choi, Yosoon

2017-01-01

To develop appropriate measures to prevent soil contamination in abandoned mining areas, an understanding of the spatial variation of the potentially toxic trace elements (PTEs) in the soil is necessary. For the purpose of effective soil sampling, this study uses hot spot analysis, which calculates a z-score based on the Getis-Ord Gi* statistic to identify a statistically significant hot spot sample. To constitute a statistically significant hot spot, a feature with a high value should also be surrounded by other features with high values. Using relatively cost- and time-effective portable X-ray fluorescence (PXRF) analysis, sufficient input data are acquired from the Busan abandoned mine and used for hot spot analysis. To calibrate the PXRF data, which have a relatively low accuracy, the PXRF analysis data are transformed using the inductively coupled plasma atomic emission spectrometry (ICP-AES) data. The transformed PXRF data of the Busan abandoned mine are classified into four groups according to their normalized content and z-scores: high content with a high z-score (HH), high content with a low z-score (HL), low content with a high z-score (LH), and low content with a low z-score (LL). The HL and LH cases may be due to measurement errors. Additional or complementary surveys are required for the areas surrounding these suspect samples or for significant hot spot areas. The soil sampling is conducted according to a four-phase procedure in which the hot spot analysis and proposed group classification method are employed to support the development of a sampling plan for the following phase. Overall, 30, 50, 80, and 100 samples are investigated and analyzed in phases 1–4, respectively. The method implemented in this case study may be utilized in the field for the assessment of statistically significant soil contamination and the identification of areas for which an additional survey is required. PMID:28629168

Assessing Statistically Significant Heavy-Metal Concentrations in Abandoned Mine Areas via Hot Spot Analysis of Portable XRF Data.

PubMed

Kim, Sung-Min; Choi, Yosoon

2017-06-18

To develop appropriate measures to prevent soil contamination in abandoned mining areas, an understanding of the spatial variation of the potentially toxic trace elements (PTEs) in the soil is necessary. For the purpose of effective soil sampling, this study uses hot spot analysis, which calculates a z -score based on the Getis-Ord Gi* statistic to identify a statistically significant hot spot sample. To constitute a statistically significant hot spot, a feature with a high value should also be surrounded by other features with high values. Using relatively cost- and time-effective portable X-ray fluorescence (PXRF) analysis, sufficient input data are acquired from the Busan abandoned mine and used for hot spot analysis. To calibrate the PXRF data, which have a relatively low accuracy, the PXRF analysis data are transformed using the inductively coupled plasma atomic emission spectrometry (ICP-AES) data. The transformed PXRF data of the Busan abandoned mine are classified into four groups according to their normalized content and z -scores: high content with a high z -score (HH), high content with a low z -score (HL), low content with a high z -score (LH), and low content with a low z -score (LL). The HL and LH cases may be due to measurement errors. Additional or complementary surveys are required for the areas surrounding these suspect samples or for significant hot spot areas. The soil sampling is conducted according to a four-phase procedure in which the hot spot analysis and proposed group classification method are employed to support the development of a sampling plan for the following phase. Overall, 30, 50, 80, and 100 samples are investigated and analyzed in phases 1-4, respectively. The method implemented in this case study may be utilized in the field for the assessment of statistically significant soil contamination and the identification of areas for which an additional survey is required.

Predictive modeling of outcomes following definitive chemoradiotherapy for oropharyngeal cancer based on FDG-PET image characteristics

NASA Astrophysics Data System (ADS)

Folkert, Michael R.; Setton, Jeremy; Apte, Aditya P.; Grkovski, Milan; Young, Robert J.; Schöder, Heiko; Thorstad, Wade L.; Lee, Nancy Y.; Deasy, Joseph O.; Oh, Jung Hun

2017-07-01

In this study, we investigate the use of imaging feature-based outcomes research (‘radiomics’) combined with machine learning techniques to develop robust predictive models for the risk of all-cause mortality (ACM), local failure (LF), and distant metastasis (DM) following definitive chemoradiation therapy (CRT). One hundred seventy four patients with stage III-IV oropharyngeal cancer (OC) treated at our institution with CRT with retrievable pre- and post-treatment 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) scans were identified. From pre-treatment PET scans, 24 representative imaging features of FDG-avid disease regions were extracted. Using machine learning-based feature selection methods, multiparameter logistic regression models were built incorporating clinical factors and imaging features. All model building methods were tested by cross validation to avoid overfitting, and final outcome models were validated on an independent dataset from a collaborating institution. Multiparameter models were statistically significant on 5 fold cross validation with the area under the receiver operating characteristic curve (AUC)  =  0.65 (p  =  0.004), 0.73 (p  =  0.026), and 0.66 (p  =  0.015) for ACM, LF, and DM, respectively. The model for LF retained significance on the independent validation cohort with AUC  =  0.68 (p  =  0.029) whereas the models for ACM and DM did not reach statistical significance, but resulted in comparable predictive power to the 5 fold cross validation with AUC  =  0.60 (p  =  0.092) and 0.65 (p  =  0.062), respectively. In the largest study of its kind to date, predictive features including increasing metabolic tumor volume, increasing image heterogeneity, and increasing tumor surface irregularity significantly correlated to mortality, LF, and DM on 5 fold cross validation in a relatively uniform single-institution cohort. The LF model also retained significance in an independent population.

Bayesian depth estimation from monocular natural images.

PubMed

Su, Che-Chun; Cormack, Lawrence K; Bovik, Alan C

2017-05-01

Estimating an accurate and naturalistic dense depth map from a single monocular photographic image is a difficult problem. Nevertheless, human observers have little difficulty understanding the depth structure implied by photographs. Two-dimensional (2D) images of the real-world environment contain significant statistical information regarding the three-dimensional (3D) structure of the world that the vision system likely exploits to compute perceived depth, monocularly as well as binocularly. Toward understanding how this might be accomplished, we propose a Bayesian model of monocular depth computation that recovers detailed 3D scene structures by extracting reliable, robust, depth-sensitive statistical features from single natural images. These features are derived using well-accepted univariate natural scene statistics (NSS) models and recent bivariate/correlation NSS models that describe the relationships between 2D photographic images and their associated depth maps. This is accomplished by building a dictionary of canonical local depth patterns from which NSS features are extracted as prior information. The dictionary is used to create a multivariate Gaussian mixture (MGM) likelihood model that associates local image features with depth patterns. A simple Bayesian predictor is then used to form spatial depth estimates. The depth results produced by the model, despite its simplicity, correlate well with ground-truth depths measured by a current-generation terrestrial light detection and ranging (LIDAR) scanner. Such a strong form of statistical depth information could be used by the visual system when creating overall estimated depth maps incorporating stereopsis, accommodation, and other conditions. Indeed, even in isolation, the Bayesian predictor delivers depth estimates that are competitive with state-of-the-art "computer vision" methods that utilize highly engineered image features and sophisticated machine learning algorithms.

ECG Identification System Using Neural Network with Global and Local Features

ERIC Educational Resources Information Center

Tseng, Kuo-Kun; Lee, Dachao; Chen, Charles

2016-01-01

This paper proposes a human identification system via extracted electrocardiogram (ECG) signals. Two hierarchical classification structures based on global shape feature and local statistical feature is used to extract ECG signals. Global shape feature represents the outline information of ECG signals and local statistical feature extracts the…

Parallel object-oriented decision tree system

DOEpatents

Kamath,; Chandrika, Cantu-Paz [Dublin, CA; Erick, [Oakland, CA

2006-02-28

A data mining decision tree system that uncovers patterns, associations, anomalies, and other statistically significant structures in data by reading and displaying data files, extracting relevant features for each of the objects, and using a method of recognizing patterns among the objects based upon object features through a decision tree that reads the data, sorts the data if necessary, determines the best manner to split the data into subsets according to some criterion, and splits the data.

Gender differences in knee morphology and the prospects for implant design in total knee replacement.

PubMed

Asseln, Malte; Hänisch, Christoph; Schick, Fabian; Radermacher, Klaus

2018-05-14

Morphological differences between female and male knees have been reported in the literature, which led to the development of so-called gender-specific implants. However, detailed morphological descriptions covering the entire joint are rare and little is known regarding whether gender differences are real sexual dimorphisms or can be explained by overall differences in size. We comprehensively analysed knee morphology using 33 features of the femur and 21 features of the tibia to quantify knee shape. The landmark recognition and feature extraction based on three-dimensional surface data were fully automatically applied to 412 pathological (248 female and 164 male) knees undergoing total knee arthroplasty. Subsequently, an exploratory statistical analysis was performed and linear correlation analysis was used to investigate normalization factors and gender-specific differences. Statistically significant differences between genders were observed. These were pronounced for distance measurements and negligible for angular (relative) measurements. Female knees were significantly narrower at the same depth compared to male knees. The correlation analysis showed that linear correlations were higher for distance measurements defined in the same direction. After normalizing the distance features according to overall dimensions in the direction of their definition, gender-specific differences disappeared or were smaller than the related confidence intervals. Implants should not be linearly scaled according to one dimension. Instead, features in medial/lateral and anterior/posterior directions should be normalized separately (non-isotropic scaling). However, large inter-individual variations of the features remain after normalization, suggesting that patient-specific design solutions are required for an improved implant design, regardless of gender. Copyright © 2018 Elsevier B.V. All rights reserved.

Statistical-techniques-based computer-aided diagnosis (CAD) using texture feature analysis: application in computed tomography (CT) imaging to fatty liver disease

NASA Astrophysics Data System (ADS)

Chung, Woon-Kwan; Park, Hyong-Hu; Im, In-Chul; Lee, Jae-Seung; Goo, Eun-Hoe; Dong, Kyung-Rae

2012-09-01

This paper proposes a computer-aided diagnosis (CAD) system based on texture feature analysis and statistical wavelet transformation technology to diagnose fatty liver disease with computed tomography (CT) imaging. In the target image, a wavelet transformation was performed for each lesion area to set the region of analysis (ROA, window size: 50 × 50 pixels) and define the texture feature of a pixel. Based on the extracted texture feature values, six parameters (average gray level, average contrast, relative smoothness, skewness, uniformity, and entropy) were determined to calculate the recognition rate for a fatty liver. In addition, a multivariate analysis of the variance (MANOVA) method was used to perform a discriminant analysis to verify the significance of the extracted texture feature values and the recognition rate for a fatty liver. According to the results, each texture feature value was significant for a comparison of the recognition rate for a fatty liver ( p < 0.05). Furthermore, the F-value, which was used as a scale for the difference in recognition rates, was highest in the average gray level, relatively high in the skewness and the entropy, and relatively low in the uniformity, the relative smoothness and the average contrast. The recognition rate for a fatty liver had the same scale as that for the F-value, showing 100% (average gray level) at the maximum and 80% (average contrast) at the minimum. Therefore, the recognition rate is believed to be a useful clinical value for the automatic detection and computer-aided diagnosis (CAD) using the texture feature value. Nevertheless, further study on various diseases and singular diseases will be needed in the future.

The clinical implications of high levels of autism spectrum disorder features in anorexia nervosa: a pilot study.

PubMed

Huke, Vanessa; Turk, Jeremy; Saeidi, Saeideh; Kent, Andrew; Morgan, John F

2014-03-01

This study examined autism spectrum disorder (ASD) features in relation to treatment completion and eating disorder psychopathology in anorexia nervosa (AN). Thirty-two adult women were recruited from specialist eating disorder services. Features of ASD and disordered eating were measured. Premature termination of treatment was recorded to explore whether ASD traits had impact on early discharge. A healthy control group was also recruited to investigate ASD traits between clinical and nonclinical samples. Significant differences were found between the AN group and the healthy control group in obsessive-compulsive disorder traits, depression and anxiety and ASD traits, with significant differences between groups in Social Skill and Attention Switching. The AN group reported no significant relationship between disordered eating severity and ASD traits. No significant effect was found between ASD features and treatment completion. Raw data on premature termination of treatment, despite no statistic impact, showed that seven out of the eight participants with high features of ASD completed treatment as planned compared with 50% of those with low ASD traits. Unexpectedly, this suggests enhanced treatment adherence in ASD. Copyright © 2013 John Wiley & Sons, Ltd and Eating Disorders Association.

Deep convolutional neural network for mammographic density segmentation

NASA Astrophysics Data System (ADS)

Wei, Jun; Li, Songfeng; Chan, Heang-Ping; Helvie, Mark A.; Roubidoux, Marilyn A.; Lu, Yao; Zhou, Chuan; Hadjiiski, Lubomir; Samala, Ravi K.

2018-02-01

Breast density is one of the most significant factors for cancer risk. In this study, we proposed a supervised deep learning approach for automated estimation of percentage density (PD) on digital mammography (DM). The deep convolutional neural network (DCNN) was trained to estimate a probability map of breast density (PMD). PD was calculated as the ratio of the dense area to the breast area based on the probability of each pixel belonging to dense region or fatty region at a decision threshold of 0.5. The DCNN estimate was compared to a feature-based statistical learning approach, in which gray level, texture and morphological features were extracted from each ROI and the least absolute shrinkage and selection operator (LASSO) was used to select and combine the useful features to generate the PMD. The reference PD of each image was provided by two experienced MQSA radiologists. With IRB approval, we retrospectively collected 347 DMs from patient files at our institution. The 10-fold cross-validation results showed a strong correlation r=0.96 between the DCNN estimation and interactive segmentation by radiologists while that of the feature-based statistical learning approach vs radiologists' segmentation had a correlation r=0.78. The difference between the segmentation by DCNN and by radiologists was significantly smaller than that between the feature-based learning approach and radiologists (p < 0.0001) by two-tailed paired t-test. This study demonstrated that the DCNN approach has the potential to replace radiologists' interactive thresholding in PD estimation on DMs.

SU-F-R-20: Image Texture Features Correlate with Time to Local Failure in Lung SBRT Patients

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

Andrews, M; Abazeed, M; Woody, N

Purpose: To explore possible correlation between CT image-based texture and histogram features and time-to-local-failure in early stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiotherapy (SBRT).Methods and Materials: From an IRB-approved lung SBRT registry for patients treated between 2009–2013 we selected 48 (20 male, 28 female) patients with local failure. Median patient age was 72.3±10.3 years. Mean time to local failure was 15 ± 7.1 months. Physician-contoured gross tumor volumes (GTV) on the planning CT images were processed and 3D gray-level co-occurrence matrix (GLCM) based texture and histogram features were calculated in Matlab. Data were exported tomore » R and a multiple linear regression model was used to examine the relationship between texture features and time-to-local-failure. Results: Multiple linear regression revealed that entropy (p=0.0233, multiple R2=0.60) from GLCM-based texture analysis and the standard deviation (p=0.0194, multiple R2=0.60) from the histogram-based features were statistically significantly correlated with the time-to-local-failure. Conclusion: Image-based texture analysis can be used to predict certain aspects of treatment outcomes of NSCLC patients treated with SBRT. We found entropy and standard deviation calculated for the GTV on the CT images displayed a statistically significant correlation with and time-to-local-failure in lung SBRT patients.« less

Association between C-reactive protein and features of the metabolic syndrome: a population-based study.

PubMed

Fröhlich, M; Imhof, A; Berg, G; Hutchinson, W L; Pepys, M B; Boeing, H; Muche, R; Brenner, H; Koenig, W

2000-12-01

To assess the association of circulating levels of C-reactive protein, a sensitive systemic marker of inflammation, with different components of the metabolic syndrome. Total cholesterol (TC), HDL cholesterol, triglycerides, uric acid, BMI , and prevalence of diabetes and hypertension were assessed in 747 men and 956 women aged 18-89 years who were participating in the population-based National Health and Nutrition Survey, which was carried out in former West Germany in 1987-1988. There was a statistically significant positive crude correlation between C-reactive protein and TC (R = 0.19), TG (R = 0.29), BMI (R = 0.32), glucose (R = 0.11), and uric acid (R = 0.14) (all P < 0.0001). A negative correlation was found between C-reactive protein and HDL cholesterol (R = 0.13, P < 0.0001). The age-adjusted geometric means of C-reactive protein concentrations in subjects grouped according to the presence of 0-1, 2-3, and > or =4 features of the metabolic syndrome were 1.11, 1.27, and 2.16 mg/l, respectively, with a statistically highly significant trend (P < 0.0001). The data suggest that a variety of features of the metabolic syndrome are associated with a systemic inflammatory response.

Prostate segmentation in MR images using discriminant boundary features.

PubMed

Yang, Meijuan; Li, Xuelong; Turkbey, Baris; Choyke, Peter L; Yan, Pingkun

2013-02-01

Segmentation of the prostate in magnetic resonance image has become more in need for its assistance to diagnosis and surgical planning of prostate carcinoma. Due to the natural variability of anatomical structures, statistical shape model has been widely applied in medical image segmentation. Robust and distinctive local features are critical for statistical shape model to achieve accurate segmentation results. The scale invariant feature transformation (SIFT) has been employed to capture the information of the local patch surrounding the boundary. However, when SIFT feature being used for segmentation, the scale and variance are not specified with the location of the point of interest. To deal with it, the discriminant analysis in machine learning is introduced to measure the distinctiveness of the learned SIFT features for each landmark directly and to make the scale and variance adaptive to the locations. As the gray values and gradients vary significantly over the boundary of the prostate, separate appearance descriptors are built for each landmark and then optimized. After that, a two stage coarse-to-fine segmentation approach is carried out by incorporating the local shape variations. Finally, the experiments on prostate segmentation from MR image are conducted to verify the efficiency of the proposed algorithms.

Recipient area folliculitis after follicular-unit transplantation: characterization of clinical features and analysis of associated factors.

PubMed

Bunagan, M J Kristine S; Pathomvanich, Damkerng; Laorwong, Kongkiat

2010-07-01

Postoperative recipient-area folliculitis may be a cause of less or delayed growth of transplanted hair and an obvious cause of distress to the patient. No study has been done to elaborate on its clinical features and assess possible factors that may correlate with its occurrence. To study the clinical features and possible factors that may be associated with the development of recipient-area folliculitis after follicular-unit transplantation (FUT). Retrospective analysis of 27 patients who developed folliculitis after FUT and 28 patients without such complication. Lesion onset ranged from 2 days to 6 months after FUT (mean 1.44 months). Lesions were mostly pustules that resolved without sequela. Statistical analysis showed that, in terms of patient characteristics (e.g., hair features, scalp condition) and the number of grafts transplanted, there was no statistically significant difference in assessed parameters between those with and without folliculitis (p<.05). Main clinical features of postoperative folliculitis consist mostly of few to moderate self-limited pustules. In this study, regardless of management, lesions healed without scarring and without affecting graft growth. Neither patient characteristics nor number of grafts transplanted was associated with this complication.

Community Design Impacts on Health Habits in Low-income Southern Nevadans.

PubMed

Coughenour, Courtney; Burns, Mackenzie S

2016-07-01

The purposes of this exploratory study were to: (1) characterize selected community design features; and (2) determine the relationship between select features and physical activity (PA) levels and nutrition habits for a small sample of low-income southern Nevadans. Secondary analysis was conducted on data from selected participants of the Nevada Healthy Homes Partnership program; self-report data on PA and diet habits were compared to national guidelines. Community design features were identified via GIS within a one-mile radius of participants' homes. Descriptive statistics characterized these features and chi-square analyses were conducted to determine the relationship between select features and habits. Data from 71 participants were analyzed; the majority failed to reach either PA or fruit and vegetable guidelines (81.7% and 93.0%, respectively). Many neighborhoods were absent of parks (71.8%), trailheads (36.6%), or pay-for-use PA facilities (47.9%). The mean number of grocery stores was 3.4 ± 2.3 per neighborhood. Chi-square analyses were not statistically significant. Findings were insufficient to make meaningful conclusions, but support the need for health promotion to meet guidelines. More research is needed to assess the impact of health-promoting community design and healthy behaviors, particularly in vulnerable populations.

Prediction, Diagnosis, and Casual Thinking in Forecasting.

DTIC Science & Technology

1981-09-03

diagnostic process. However, a significant feature of causal/diagnostic thinking is the remarkable speed and fluency which people seem to have for generating...The cement of the universe: A study of causation. Oxford: Clarendon Press. Meehl, Paul E., (1954), Clinical versus statistical prediction: A

Classifying Human Voices by Using Hybrid SFX Time-Series Preprocessing and Ensemble Feature Selection

PubMed Central

Wong, Raymond

2013-01-01

Voice biometrics is one kind of physiological characteristics whose voice is different for each individual person. Due to this uniqueness, voice classification has found useful applications in classifying speakers' gender, mother tongue or ethnicity (accent), emotion states, identity verification, verbal command control, and so forth. In this paper, we adopt a new preprocessing method named Statistical Feature Extraction (SFX) for extracting important features in training a classification model, based on piecewise transformation treating an audio waveform as a time-series. Using SFX we can faithfully remodel statistical characteristics of the time-series; together with spectral analysis, a substantial amount of features are extracted in combination. An ensemble is utilized in selecting only the influential features to be used in classification model induction. We focus on the comparison of effects of various popular data mining algorithms on multiple datasets. Our experiment consists of classification tests over four typical categories of human voice data, namely, Female and Male, Emotional Speech, Speaker Identification, and Language Recognition. The experiments yield encouraging results supporting the fact that heuristically choosing significant features from both time and frequency domains indeed produces better performance in voice classification than traditional signal processing techniques alone, like wavelets and LPC-to-CC. PMID:24288684

Texture Feature Analysis for Different Resolution Level of Kidney Ultrasound Images

NASA Astrophysics Data System (ADS)

Kairuddin, Wan Nur Hafsha Wan; Mahmud, Wan Mahani Hafizah Wan

2017-08-01

Image feature extraction is a technique to identify the characteristic of the image. The objective of this work is to discover the texture features that best describe a tissue characteristic of a healthy kidney from ultrasound (US) image. Three ultrasound machines that have different specifications are used in order to get a different quality (different resolution) of the image. Initially, the acquired images are pre-processed to de-noise the speckle to ensure the image preserve the pixels in a region of interest (ROI) for further extraction. Gaussian Low- pass Filter is chosen as the filtering method in this work. 150 of enhanced images then are segmented by creating a foreground and background of image where the mask is created to eliminate some unwanted intensity values. Statistical based texture features method is used namely Intensity Histogram (IH), Gray-Level Co-Occurance Matrix (GLCM) and Gray-level run-length matrix (GLRLM).This method is depends on the spatial distribution of intensity values or gray levels in the kidney region. By using One-Way ANOVA in SPSS, the result indicated that three features (Contrast, Difference Variance and Inverse Difference Moment Normalized) from GLCM are not statistically significant; this concludes that these three features describe a healthy kidney characteristics regardless of the ultrasound image quality.

Face detection on distorted images using perceptual quality-aware features

NASA Astrophysics Data System (ADS)

Gunasekar, Suriya; Ghosh, Joydeep; Bovik, Alan C.

2014-02-01

We quantify the degradation in performance of a popular and effective face detector when human-perceived image quality is degraded by distortions due to additive white gaussian noise, gaussian blur or JPEG compression. It is observed that, within a certain range of perceived image quality, a modest increase in image quality can drastically improve face detection performance. These results can be used to guide resource or bandwidth allocation in a communication/delivery system that is associated with face detection tasks. A new face detector based on QualHOG features is also proposed that augments face-indicative HOG features with perceptual quality-aware spatial Natural Scene Statistics (NSS) features, yielding improved tolerance against image distortions. The new detector provides statistically significant improvements over a strong baseline on a large database of face images representing a wide range of distortions. To facilitate this study, we created a new Distorted Face Database, containing face and non-face patches from images impaired by a variety of common distortion types and levels. This new dataset is available for download and further experimentation at www.ideal.ece.utexas.edu/˜suriya/DFD/.

Association between traditional clinical high-risk features and gene expression profile classification in uveal melanoma.

PubMed

Nguyen, Brandon T; Kim, Ryan S; Bretana, Maria E; Kegley, Eric; Schefler, Amy C

2018-02-01

To evaluate the association between traditional clinical high-risk features of uveal melanoma patients and gene expression profile (GEP). This was a retrospective, single-center, case series of patients with uveal melanoma. Eighty-three patients met inclusion criteria for the study. Patients were examined for the following clinical risk factors: drusen/retinal pigment epithelium (RPE) changes, vascularity on B-scan, internal reflectivity on A-scan, subretinal fluid (SRF), orange pigment, apical tumor height/thickness, and largest basal dimensions (LBD). A novel point system was created to grade the high-risk clinical features of each tumor. Further analyses were performed to assess the degree of association between GEP and each individual risk factor, total clinical risk score, vascularity, internal reflectivity, American Joint Committee on Cancer (AJCC) tumor stage classification, apical tumor height/thickness, and LBD. Of the 83 total patients, 41 were classified as GEP class 1A, 17 as class 1B, and 25 as class 2. The presence of orange pigment, SRF, low internal reflectivity and vascularity on ultrasound, and apical tumor height/thickness ≥ 2 mm were not statistically significantly associated with GEP class. Lack of drusen/RPE changes demonstrated a trend toward statistical association with GEP class 2 compared to class 1A/1B. LBD and advancing AJCC stage was statistically associated with higher GEP class. In this cohort, AJCC stage classification and LBD were the only clinical features statistically associated with GEP class. Clinicians should use caution when inferring the growth potential of melanocytic lesions solely from traditional funduscopic and ultrasonographic risk factors without GEP data.

Bag-of-features approach for improvement of lung tissue classification in diffuse lung disease

NASA Astrophysics Data System (ADS)

Kato, Noriji; Fukui, Motofumi; Isozaki, Takashi

2009-02-01

Many automated techniques have been proposed to classify diffuse lung disease patterns. Most of the techniques utilize texture analysis approaches with second and higher order statistics, and show successful classification result among various lung tissue patterns. However, the approaches do not work well for the patterns with inhomogeneous texture distribution within a region of interest (ROI), such as reticular and honeycombing patterns, because the statistics can only capture averaged feature over the ROI. In this work, we have introduced the bag-of-features approach to overcome this difficulty. In the approach, texture images are represented as histograms or distributions of a few basic primitives, which are obtained by clustering local image features. The intensity descriptor and the Scale Invariant Feature Transformation (SIFT) descriptor are utilized to extract the local features, which have significant discriminatory power due to their specificity to a particular image class. In contrast, the drawback of the local features is lack of invariance under translation and rotation. We improved the invariance by sampling many local regions so that the distribution of the local features is unchanged. We evaluated the performance of our system in the classification task with 5 image classes (ground glass, reticular, honeycombing, emphysema, and normal) using 1109 ROIs from 211 patients. Our system achieved high classification accuracy of 92.8%, which is superior to that of the conventional system with the gray level co-occurrence matrix (GLCM) feature especially for inhomogeneous texture patterns.

Can Laws Be a Potential PET Image Texture Analysis Approach for Evaluation of Tumor Heterogeneity and Histopathological Characteristics in NSCLC?

PubMed

Karacavus, Seyhan; Yılmaz, Bülent; Tasdemir, Arzu; Kayaaltı, Ömer; Kaya, Eser; İçer, Semra; Ayyıldız, Oguzhan

2018-04-01

We investigated the association between the textural features obtained from 18 F-FDG images, metabolic parameters (SUVmax , SUVmean, MTV, TLG), and tumor histopathological characteristics (stage and Ki-67 proliferation index) in non-small cell lung cancer (NSCLC). The FDG-PET images of 67 patients with NSCLC were evaluated. MATLAB technical computing language was employed in the extraction of 137 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), and Laws' texture filters. Textural features and metabolic parameters were statistically analyzed in terms of good discrimination power between tumor stages, and selected features/parameters were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). We showed that one textural feature (gray-level nonuniformity, GLN) obtained using GLRLM approach and nine textural features using Laws' approach were successful in discriminating all tumor stages, unlike metabolic parameters. There were significant correlations between Ki-67 index and some of the textural features computed using Laws' method (r = 0.6, p = 0.013). In terms of automatic classification of tumor stage, the accuracy was approximately 84% with k-NN classifier (k = 3) and SVM, using selected five features. Texture analysis of FDG-PET images has a potential to be an objective tool to assess tumor histopathological characteristics. The textural features obtained using Laws' approach could be useful in the discrimination of tumor stage.

PSYCHOLOGY. Estimating the reproducibility of psychological science.

PubMed

2015-08-28

Reproducibility is a defining feature of science, but the extent to which it characterizes current research is unknown. We conducted replications of 100 experimental and correlational studies published in three psychology journals using high-powered designs and original materials when available. Replication effects were half the magnitude of original effects, representing a substantial decline. Ninety-seven percent of original studies had statistically significant results. Thirty-six percent of replications had statistically significant results; 47% of original effect sizes were in the 95% confidence interval of the replication effect size; 39% of effects were subjectively rated to have replicated the original result; and if no bias in original results is assumed, combining original and replication results left 68% with statistically significant effects. Correlational tests suggest that replication success was better predicted by the strength of original evidence than by characteristics of the original and replication teams. Copyright © 2015, American Association for the Advancement of Science.

Computation and evaluation of features of surface electromyogram to identify the force of muscle contraction and muscle fatigue.

PubMed

Arjunan, Sridhar P; Kumar, Dinesh K; Naik, Ganesh

2014-01-01

The relationship between force of muscle contraction and muscle fatigue with six different features of surface electromyogram (sEMG) was determined by conducting experiments on thirty-five volunteers. The participants performed isometric contractions at 50%, 75%, and 100% of their maximum voluntary contraction (MVC). Six features were considered in this study: normalised spectral index (NSM5), median frequency, root mean square, waveform length, normalised root mean square (NRMS), and increase in synchronization (IIS) index. Analysis of variance (ANOVA) and linear regression analysis were performed to determine the significance of the feature with respect to the three factors: muscle force, muscle fatigue, and subject. The results show that IIS index of sEMG had the highest correlation with muscle fatigue and the relationship was statistically significant (P < 0.01), while NSM5 associated best with level of muscle contraction (%MVC) (P < 0.01). Both of these features were not affected by the intersubject variations (P > 0.05).

Computation and Evaluation of Features of Surface Electromyogram to Identify the Force of Muscle Contraction and Muscle Fatigue

PubMed Central

Arjunan, Sridhar P.; Kumar, Dinesh K.; Naik, Ganesh

2014-01-01

The relationship between force of muscle contraction and muscle fatigue with six different features of surface electromyogram (sEMG) was determined by conducting experiments on thirty-five volunteers. The participants performed isometric contractions at 50%, 75%, and 100% of their maximum voluntary contraction (MVC). Six features were considered in this study: normalised spectral index (NSM5), median frequency, root mean square, waveform length, normalised root mean square (NRMS), and increase in synchronization (IIS) index. Analysis of variance (ANOVA) and linear regression analysis were performed to determine the significance of the feature with respect to the three factors: muscle force, muscle fatigue, and subject. The results show that IIS index of sEMG had the highest correlation with muscle fatigue and the relationship was statistically significant (P < 0.01), while NSM5 associated best with level of muscle contraction (%MVC) (P < 0.01). Both of these features were not affected by the intersubject variations (P > 0.05). PMID:24995275

Grading of Gliomas by Using Radiomic Features on Multiple Magnetic Resonance Imaging (MRI) Sequences.

PubMed

Qin, Jiang-Bo; Liu, Zhenyu; Zhang, Hui; Shen, Chen; Wang, Xiao-Chun; Tan, Yan; Wang, Shuo; Wu, Xiao-Feng; Tian, Jie

2017-05-07

BACKGROUND Gliomas are the most common primary brain neoplasms. Misdiagnosis occurs in glioma grading due to an overlap in conventional MRI manifestations. The aim of the present study was to evaluate the power of radiomic features based on multiple MRI sequences - T2-Weighted-Imaging-FLAIR (FLAIR), T1-Weighted-Imaging-Contrast-Enhanced (T1-CE), and Apparent Diffusion Coefficient (ADC) map - in glioma grading, and to improve the power of glioma grading by combining features. MATERIAL AND METHODS Sixty-six patients with histopathologically proven gliomas underwent T2-FLAIR and T1WI-CE sequence scanning with some patients (n=63) also undergoing DWI scanning. A total of 114 radiomic features were derived with radiomic methods by using in-house software. All radiomic features were compared between high-grade gliomas (HGGs) and low-grade gliomas (LGGs). Features with significant statistical differences were selected for receiver operating characteristic (ROC) curve analysis. The relationships between significantly different radiomic features and glial fibrillary acidic protein (GFAP) expression were evaluated. RESULTS A total of 8 radiomic features from 3 MRI sequences displayed significant differences between LGGs and HGGs. FLAIR GLCM Cluster Shade, T1-CE GLCM Entropy, and ADC GLCM Homogeneity were the best features to use in differentiating LGGs and HGGs in each MRI sequence. The combined feature was best able to differentiate LGGs and HGGs, which improved the accuracy of glioma grading compared to the above features in each MRI sequence. A significant correlation was found between GFAP and T1-CE GLCM Entropy, as well as between GFAP and ADC GLCM Homogeneity. CONCLUSIONS The combined radiomic feature had the highest efficacy in distinguishing LGGs from HGGs.

Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning.

PubMed

Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego

2016-06-17

Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults.

Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning

PubMed Central

Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego

2016-01-01

Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults. PMID:27322273

Structural health monitoring feature design by genetic programming

NASA Astrophysics Data System (ADS)

Harvey, Dustin Y.; Todd, Michael D.

2014-09-01

Structural health monitoring (SHM) systems provide real-time damage and performance information for civil, aerospace, and other high-capital or life-safety critical structures. Conventional data processing involves pre-processing and extraction of low-dimensional features from in situ time series measurements. The features are then input to a statistical pattern recognition algorithm to perform the relevant classification or regression task necessary to facilitate decisions by the SHM system. Traditional design of signal processing and feature extraction algorithms can be an expensive and time-consuming process requiring extensive system knowledge and domain expertise. Genetic programming, a heuristic program search method from evolutionary computation, was recently adapted by the authors to perform automated, data-driven design of signal processing and feature extraction algorithms for statistical pattern recognition applications. The proposed method, called Autofead, is particularly suitable to handle the challenges inherent in algorithm design for SHM problems where the manifestation of damage in structural response measurements is often unclear or unknown. Autofead mines a training database of response measurements to discover information-rich features specific to the problem at hand. This study provides experimental validation on three SHM applications including ultrasonic damage detection, bearing damage classification for rotating machinery, and vibration-based structural health monitoring. Performance comparisons with common feature choices for each problem area are provided demonstrating the versatility of Autofead to produce significant algorithm improvements on a wide range of problems.

The Optical Gravitational Lensing Experiment

NASA Technical Reports Server (NTRS)

Udalski, A.; Szymanski, M.; Kaluzny, J.; Kubiak, M.; Mateo, Mario

1992-01-01

The technical features are described of the Optical Gravitational Lensing Experiment, which aims to detect a statistically significant number of microlensing events toward the Galactic bulge. Clusters of galaxies observed during the 1992 season are listed and discussed and the reduction methods are described. Future plans are addressed.

Classification of emotional states from electrocardiogram signals: a non-linear approach based on hurst

PubMed Central

2013-01-01

Background Identifying the emotional state is helpful in applications involving patients with autism and other intellectual disabilities; computer-based training, human computer interaction etc. Electrocardiogram (ECG) signals, being an activity of the autonomous nervous system (ANS), reflect the underlying true emotional state of a person. However, the performance of various methods developed so far lacks accuracy, and more robust methods need to be developed to identify the emotional pattern associated with ECG signals. Methods Emotional ECG data was obtained from sixty participants by inducing the six basic emotional states (happiness, sadness, fear, disgust, surprise and neutral) using audio-visual stimuli. The non-linear feature ‘Hurst’ was computed using Rescaled Range Statistics (RRS) and Finite Variance Scaling (FVS) methods. New Hurst features were proposed by combining the existing RRS and FVS methods with Higher Order Statistics (HOS). The features were then classified using four classifiers – Bayesian Classifier, Regression Tree, K- nearest neighbor and Fuzzy K-nearest neighbor. Seventy percent of the features were used for training and thirty percent for testing the algorithm. Results Analysis of Variance (ANOVA) conveyed that Hurst and the proposed features were statistically significant (p < 0.001). Hurst computed using RRS and FVS methods showed similar classification accuracy. The features obtained by combining FVS and HOS performed better with a maximum accuracy of 92.87% and 76.45% for classifying the six emotional states using random and subject independent validation respectively. Conclusions The results indicate that the combination of non-linear analysis and HOS tend to capture the finer emotional changes that can be seen in healthy ECG data. This work can be further fine tuned to develop a real time system. PMID:23680041

Task-induced frequency modulation features for brain-computer interfacing

NASA Astrophysics Data System (ADS)

Jayaram, Vinay; Hohmann, Matthias; Just, Jennifer; Schölkopf, Bernhard; Grosse-Wentrup, Moritz

2017-10-01

Objective. Task-induced amplitude modulation of neural oscillations is routinely used in brain-computer interfaces (BCIs) for decoding subjects’ intents, and underlies some of the most robust and common methods in the field, such as common spatial patterns and Riemannian geometry. While there has been some interest in phase-related features for classification, both techniques usually presuppose that the frequencies of neural oscillations remain stable across various tasks. We investigate here whether features based on task-induced modulation of the frequency of neural oscillations enable decoding of subjects’ intents with an accuracy comparable to task-induced amplitude modulation. Approach. We compare cross-validated classification accuracies using the amplitude and frequency modulated features, as well as a joint feature space, across subjects in various paradigms and pre-processing conditions. We show results with a motor imagery task, a cognitive task, and also preliminary results in patients with amyotrophic lateral sclerosis (ALS), as well as using common spatial patterns and Laplacian filtering. Main results. The frequency features alone do not significantly out-perform traditional amplitude modulation features, and in some cases perform significantly worse. However, across both tasks and pre-processing in healthy subjects the joint space significantly out-performs either the frequency or amplitude features alone. This result only does not hold for ALS patients, for whom the dataset is of insufficient size to draw any statistically significant conclusions. Significance. Task-induced frequency modulation is robust and straight forward to compute, and increases performance when added to standard amplitude modulation features across paradigms. This allows more information to be extracted from the EEG signal cheaply and can be used throughout the field of BCIs.

Task-induced frequency modulation features for brain-computer interfacing.

PubMed

Jayaram, Vinay; Hohmann, Matthias; Just, Jennifer; Schölkopf, Bernhard; Grosse-Wentrup, Moritz

2017-10-01

Task-induced amplitude modulation of neural oscillations is routinely used in brain-computer interfaces (BCIs) for decoding subjects' intents, and underlies some of the most robust and common methods in the field, such as common spatial patterns and Riemannian geometry. While there has been some interest in phase-related features for classification, both techniques usually presuppose that the frequencies of neural oscillations remain stable across various tasks. We investigate here whether features based on task-induced modulation of the frequency of neural oscillations enable decoding of subjects' intents with an accuracy comparable to task-induced amplitude modulation. We compare cross-validated classification accuracies using the amplitude and frequency modulated features, as well as a joint feature space, across subjects in various paradigms and pre-processing conditions. We show results with a motor imagery task, a cognitive task, and also preliminary results in patients with amyotrophic lateral sclerosis (ALS), as well as using common spatial patterns and Laplacian filtering. The frequency features alone do not significantly out-perform traditional amplitude modulation features, and in some cases perform significantly worse. However, across both tasks and pre-processing in healthy subjects the joint space significantly out-performs either the frequency or amplitude features alone. This result only does not hold for ALS patients, for whom the dataset is of insufficient size to draw any statistically significant conclusions. Task-induced frequency modulation is robust and straight forward to compute, and increases performance when added to standard amplitude modulation features across paradigms. This allows more information to be extracted from the EEG signal cheaply and can be used throughout the field of BCIs.

Supervised learning methods for pathological arterial pulse wave differentiation: A SVM and neural networks approach.

PubMed

Paiva, Joana S; Cardoso, João; Pereira, Tânia

2018-01-01

The main goal of this study was to develop an automatic method based on supervised learning methods, able to distinguish healthy from pathologic arterial pulse wave (APW), and those two from noisy waveforms (non-relevant segments of the signal), from the data acquired during a clinical examination with a novel optical system. The APW dataset analysed was composed by signals acquired in a clinical environment from a total of 213 subjects, including healthy volunteers and non-healthy patients. The signals were parameterised by means of 39pulse features: morphologic, time domain statistics, cross-correlation features, wavelet features. Multiclass Support Vector Machine Recursive Feature Elimination (SVM RFE) method was used to select the most relevant features. A comparative study was performed in order to evaluate the performance of the two classifiers: Support Vector Machine (SVM) and Artificial Neural Network (ANN). SVM achieved a statistically significant better performance for this problem with an average accuracy of 0.9917±0.0024 and a F-Measure of 0.9925±0.0019, in comparison with ANN, which reached the values of 0.9847±0.0032 and 0.9852±0.0031 for Accuracy and F-Measure, respectively. A significant difference was observed between the performances obtained with SVM classifier using a different number of features from the original set available. The comparison between SVM and NN allowed reassert the higher performance of SVM. The results obtained in this study showed the potential of the proposed method to differentiate those three important signal outcomes (healthy, pathologic and noise) and to reduce bias associated with clinical diagnosis of cardiovascular disease using APW. Copyright © 2017 Elsevier B.V. All rights reserved.

Evaluating, Comparing, and Interpreting Protein Domain Hierarchies

PubMed Central

2014-01-01

Abstract Arranging protein domain sequences hierarchically into evolutionarily divergent subgroups is important for investigating evolutionary history, for speeding up web-based similarity searches, for identifying sequence determinants of protein function, and for genome annotation. However, whether or not a particular hierarchy is optimal is often unclear, and independently constructed hierarchies for the same domain can often differ significantly. This article describes methods for statistically evaluating specific aspects of a hierarchy, for probing the criteria underlying its construction and for direct comparisons between hierarchies. Information theoretical notions are used to quantify the contributions of specific hierarchical features to the underlying statistical model. Such features include subhierarchies, sequence subgroups, individual sequences, and subgroup-associated signature patterns. Underlying properties are graphically displayed in plots of each specific feature's contributions, in heat maps of pattern residue conservation, in “contrast alignments,” and through cross-mapping of subgroups between hierarchies. Together, these approaches provide a deeper understanding of protein domain functional divergence, reveal uncertainties caused by inconsistent patterns of sequence conservation, and help resolve conflicts between competing hierarchies. PMID:24559108

Valuable Features in Mobile Health Apps for Patients and Consumers: Content Analysis of Apps and User Ratings

PubMed Central

2015-01-01

Background The explosion of mobile phones with app capabilities coupled with increased expectations of the patient-consumers’ role in managing their care presents a unique opportunity to use mobile health (mHealth) apps. Objectives The aim of this paper is to identify the features and characteristics most-valued by patient-consumers (“users”) that contribute positively to the rating of an app. Methods A collection of 234 apps associated with reputable health organizations found in the medical, health, and fitness categories of the Apple iTunes store and Google Play marketplace was assessed manually for the presence of 12 app features and characteristics. Regression analysis was used to determine which, if any, contributed positively to a user’s rating of the app. Results Analysis of these 12 features explained 9.3% (R 2=.093 n=234, P<.001) of the variation in an app’s rating, with only 5 reaching statistical significance. Of the 5 reaching statistical significance, plan or orders, export of data, usability, and cost contributed positively to a user’s rating, while the tracker feature detracted from it. Conclusions These findings suggest that users appreciate features that save time over current methods and identify an app as valuable when it is simple and intuitive to use, provides specific instructions to better manage a condition, and shares data with designated individuals. Although tracking is a core function of most health apps, this feature may detract from a user’s experience when not executed properly. Further investigation into mHealth app features is worthwhile given the inability of the most common features to explain a large portion of an app’s rating. In the future, studies should focus on one category in the app store, specific diseases, or desired behavior change, and methods should include measuring the quality of each feature, both through manual assessment and evaluation of user reviews. Additional investigations into understanding the impact of synergistic features, incentives, social media, and gamification are also warranted to identify possible future trends. PMID:25972309

Hematology, cytochemistry and ultrastructure of blood cells in fishing cat (Felis viverrina).

PubMed

Prihirunkit, Kreangsak; Salakij, Chaleow; Apibal, Suntaree; Narkkong, Nual Anong

2007-06-01

Hematological, cytochemical and ultrastructural features of blood cells in fishing cat (Felis viverrina) were evaluated using complete blood cell counts with routine and cytochemical blood stains, and scanning and transmission electron microscopy. No statistically significant difference was found in different genders of this animal. Unique features of blood cells in this animal were identified in hematological, cytochemical and ultrastructural studies. This study contributes to broaden hematological resources in wildlife animals and provides a guideline for identification of blood cells in the fishing cat.

Summarizing Monte Carlo Results in Methodological Research.

ERIC Educational Resources Information Center

Harwell, Michael R.

Monte Carlo studies of statistical tests are prominently featured in the methodological research literature. Unfortunately, the information from these studies does not appear to have significantly influenced methodological practice in educational and psychological research. One reason is that Monte Carlo studies lack an overarching theory to guide…

Internet marketing directed at children on food and restaurant websites in two policy environments.

PubMed

Kent, M Potvin; Dubois, L; Kent, E A; Wanless, A J

2013-04-01

Food and beverage marketing has been associated with childhood obesity yet little research has examined the influence of advertising policy on children's exposure to food/beverage marketing on the Internet. The purpose of this study was to assess the influence of Quebec's Consumer Protection Act and the self-regulatory Canadian Children's Food and Beverage Advertising Initiative (CAI) on food manufacturer and restaurant websites in Canada. A content analysis of 147 French and English language food and restaurant websites was undertaken. The presence of child-directed content was assessed and an analysis of marketing features, games and activities, child protection features, and the promotion of healthy lifestyle messages was then examined on those sites with child-directed content. There were statistically no fewer French language websites (n = 22) with child-directed content compared to English language websites (n = 27). There were no statistically significant differences in the number of the various marketing features, or in the average number of marketing features between the English and French websites. There were no fewer CAI websites (n = 14) with child-directed content compared to non-CAI websites (n = 13). The CAI sites had more healthy lifestyle messages and child protection features compared to the non-CAI sites. Systematic surveillance of the Consumer Protection Act in Quebec is recommended. In the rest of Canada, the CAI needs to be significantly expanded or replaced by regulatory measures to adequately protect children from the marketing of foods/beverages high in fat, sugar, and sodium on the Internet. Copyright © 2012 The Obesity Society.

Neuropsychological assessment of decision making in alcohol-dependent commercial pilots.

PubMed

Georgemiller, Randy; Machizawa, Sayaka; Young, Kathleen M; Martin, Cynthia N

2013-09-01

The aim of this exploratory archival study was to discern the utility of the Iowa Gambling Task (IGT) in identifying adaptive decision-making capacities among pilots with a history of alcohol dependence both with and without Cluster B personality features. Participants included 18 male airmen at the rank of captain with a history of receiving alcohol dependence treatment and subsequent referral for a fitness-for-duty evaluation. Data from prior comprehensive neuropsychological evaluations conducted in a private practice setting at the mandate of the FAA utilizing criteria outlined in the HIMS program was used. ANOVA was conducted to compare pilots with (N = 4) and without Cluster B personality features (N = 14) on measures of decisionmaking capacities, intelligence, and executive functioning. Pilots with Cluster B personality features were found to have a significantly lower Total Net T-Score on IGT (M = 35.00, SD = 9.27) than pilots without features of Cluster B (M = 56.36, SD = 9.55). Furthermore, with the exception of the first 20 cards (i.e., Net 1); the groups significantly differed in their Net scores. No statistically significant difference was found on airmen's intelligence and executive functioning. The present study found that alcohol-dependent airmen with Cluster B personality features evidenced significantly poorer decisionmaking capacities as measured by the ICT in comparison to alcohol dependent airman without Cluster B personality features. Implications and limitations of the study are discussed.

How have changes in front air bag designs affected frontal crash death rates? An update.

PubMed

Teoh, Eric R

2014-01-01

Provide updated death rates comparing latest generations of frontal air bags in fatal crashes. Rates of driver and right-front passenger deaths in frontal crashes per 10 million registered vehicle years were compared using Poisson marginal structural models for passenger vehicles equipped with air bags certified as advanced and compliant (CAC), sled-certified air bags with advanced features, and sled-certified air bags without any advanced features. Analyses of driver death rates were disaggregated by age group, gender, and belt use. CAC air bags were associated with slightly elevated frontal crash death rates for both drivers and right-front passengers compared to sled-certified air bags with advanced features, but the differences were not statistically significant. Sled-certified air bags with advanced features were associated with significant benefits for drivers and for right-front passengers compared to sled-certified air bags without advanced features. CAC air bags were associated with a significant increase in belted driver death rate and a comparable but nonsignificant decrease in unbelted driver death rate compared to sled-certified air bags with advanced features. Sled-certified air bags with advanced features were associated with a nonsignificant 2 percent increase in belted driver death rate and a significant 26 percent decrease in unbelted driver death rate, relative to sled-certified air bags without advanced features. Implementing advanced features in sled-certified air bags was beneficial overall to drivers and right-front passengers with sled-certified air bags. No overall benefit was observed for CAC air bags compared to sled-certified air bags with advanced features. Further study is needed to understand the apparent reduction in belted driver protection observed for CAC air bags.

Treatment recommendations for DSM-5-defined mixed features.

PubMed

Rosenblat, Joshua D; McIntyre, Roger S

2017-04-01

The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) mixed features specifier provides a less restrictive definition of mixed mood states, compared to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR), including mood episodes that manifest with subthreshold symptoms of the opposite mood state. A limited number of studies have assessed the efficacy of treatments specifically for DSM-5-defined mixed features in mood disorders. As such, there is currently an inadequate amount of data to appropriately inform evidence-based treatment guidelines of DSM-5 defined mixed features. However, given the high prevalence and morbidity of mixed features, treatment recommendations based on the currently available evidence along with expert opinion may be of benefit. This article serves to provide these interim treatment recommendations while humbly acknowledging the limited amount of evidence currently available. Second-generation antipsychotics (SGAs) appear to have the greatest promise in the treatment of bipolar disorder (BD) with mixed features. Conventional mood stabilizing agents (ie, lithium and divalproex) may also be of benefit; however, they have been inadequately studied. In the treatment of major depressive disorder (MDD) with mixed features, the comparable efficacy of antidepressants versus other treatments, such as SGAs, remains unknown. As such, antidepressants remain first-line treatment of MDD with or without mixed features; however, there are significant safety concerns associated with antidepressant monotherapy when mixed features are present, which merits increased monitoring. Lurasidone is the only SGA monotherapy that has been shown to be efficacious specifically in the treatment of MDD with mixed features. Further research is needed to accurately determine the efficacy, safety, and tolerability of treatments specifically for mood episodes with mixed features to adequately inform future treatment guidelines.

The relationship between 2D static features and 2D dynamic features used in gait recognition

NASA Astrophysics Data System (ADS)

Alawar, Hamad M.; Ugail, Hassan; Kamala, Mumtaz; Connah, David

2013-05-01

In most gait recognition techniques, both static and dynamic features are used to define a subject's gait signature. In this study, the existence of a relationship between static and dynamic features was investigated. The correlation coefficient was used to analyse the relationship between the features extracted from the "University of Bradford Multi-Modal Gait Database". This study includes two dimensional dynamic and static features from 19 subjects. The dynamic features were compromised of Phase-Weighted Magnitudes driven by a Fourier Transform of the temporal rotational data of a subject's joints (knee, thigh, shoulder, and elbow). The results concluded that there are eleven pairs of features that are considered significantly correlated with (p<0.05). This result indicates the existence of a statistical relationship between static and dynamics features, which challenges the results of several similar studies. These results bare great potential for further research into the area, and would potentially contribute to the creation of a gait signature using latent data.

Face-iris multimodal biometric scheme based on feature level fusion

NASA Astrophysics Data System (ADS)

Huo, Guang; Liu, Yuanning; Zhu, Xiaodong; Dong, Hongxing; He, Fei

2015-11-01

Unlike score level fusion, feature level fusion demands all the features extracted from unimodal traits with high distinguishability, as well as homogeneity and compatibility, which is difficult to achieve. Therefore, most multimodal biometric research focuses on score level fusion, whereas few investigate feature level fusion. We propose a face-iris recognition method based on feature level fusion. We build a special two-dimensional-Gabor filter bank to extract local texture features from face and iris images, and then transform them by histogram statistics into an energy-orientation variance histogram feature with lower dimensions and higher distinguishability. Finally, through a fusion-recognition strategy based on principal components analysis and support vector machine (FRSPS), feature level fusion and one-to-n identification are accomplished. The experimental results demonstrate that this method can not only effectively extract face and iris features but also provide higher recognition accuracy. Compared with some state-of-the-art fusion methods, the proposed method has a significant performance advantage.

A new unified framework for the early detection of the progression to diabetic retinopathy from fundus images.

PubMed

Leontidis, Georgios

2017-11-01

Human retina is a diverse and important tissue, vastly studied for various retinal and other diseases. Diabetic retinopathy (DR), a leading cause of blindness, is one of them. This work proposes a novel and complete framework for the accurate and robust extraction and analysis of a series of retinal vascular geometric features. It focuses on studying the registered bifurcations in successive years of progression from diabetes (no DR) to DR, in order to identify the vascular alterations. Retinal fundus images are utilised, and multiple experimental designs are employed. The framework includes various steps, such as image registration and segmentation, extraction of features, statistical analysis and classification models. Linear mixed models are utilised for making the statistical inferences, alongside the elastic-net logistic regression, boruta algorithm, and regularised random forests for the feature selection and classification phases, in order to evaluate the discriminative potential of the investigated features and also build classification models. A number of geometric features, such as the central retinal artery and vein equivalents, are found to differ significantly across the experiments and also have good discriminative potential. The classification systems yield promising results with the area under the curve values ranging from 0.821 to 0.968, across the four different investigated combinations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predicting Cell Association of Surface-Modified Nanoparticles Using Protein Corona Structure - Activity Relationships (PCSAR).

PubMed

Kamath, Padmaja; Fernandez, Alberto; Giralt, Francesc; Rallo, Robert

2015-01-01

Nanoparticles are likely to interact in real-case application scenarios with mixtures of proteins and biomolecules that will absorb onto their surface forming the so-called protein corona. Information related to the composition of the protein corona and net cell association was collected from literature for a library of surface-modified gold and silver nanoparticles. For each protein in the corona, sequence information was extracted and used to calculate physicochemical properties and statistical descriptors. Data cleaning and preprocessing techniques including statistical analysis and feature selection methods were applied to remove highly correlated, redundant and non-significant features. A weighting technique was applied to construct specific signatures that represent the corona composition for each nanoparticle. Using this basic set of protein descriptors, a new Protein Corona Structure-Activity Relationship (PCSAR) that relates net cell association with the physicochemical descriptors of the proteins that form the corona was developed and validated. The features that resulted from the feature selection were in line with already published literature, and the computational model constructed on these features had a good accuracy (R(2)LOO=0.76 and R(2)LMO(25%)=0.72) and stability, with the advantage that the fingerprints based on physicochemical descriptors were independent of the specific proteins that form the corona.

Automated thematic mapping and change detection of ERTS-A images. [farmlands, cities, and mountain identification in Utah, Washington, Arizona, and California

NASA Technical Reports Server (NTRS)

Gramenopoulos, N. (Principal Investigator)

1974-01-01

The author has identified the following significant results. A diffraction pattern analysis of MSS images led to the development of spatial signatures for farm land, urban areas and mountains. Four spatial features are employed to describe the spatial characteristics of image cells in the digital data. Three spectral features are combined with the spatial features to form a seven dimensional vector describing each cell. Then, the classification of the feature vectors is accomplished by using the maximum likelihood criterion. It was determined that the recognition accuracy with the maximum likelihood criterion depends on the statistics of the feature vectors. It was also determined that for a given geographic area the statistics of the classes remain invariable for a period of a month, but vary substantially between seasons. Three ERTS-1 images from the Phoenix, Arizona area were processed, and recognition rates between 85% and 100% were obtained for the terrain classes of desert, farms, mountains, and urban areas. To eliminate the need for training data, a new clustering algorithm has been developed. Seven ERTS-1 images from four test sites have been processed through the clustering algorithm, and high recognition rates have been achieved for all terrain classes.

Coronal Holes and Solar f -Mode Wave Scattering Off Linear Boundaries

NASA Astrophysics Data System (ADS)

Hess Webber, Shea A.

2016-11-01

Coronal holes (CHs) are solar atmospheric features that have reduced emission in the extreme ultraviolet (EUV) spectrum due to decreased plasma density along open magnetic field lines. CHs are the source of the fast solar wind, can influence other solar activity, and track the solar cycle. Our interest in them deals with boundary detection near the solar surface. Detecting CH boundaries is important for estimating their size and tracking their evolution through time, as well as for comparing the physical properties within and outside of the feature. In this thesis, we (1) investigate CHs using statistical properties and image processing techniques on EUV images to detect CH boundaries in the low corona and chromosphere. SOHO/EIT data is used to locate polar CH boundaries on the solar limb, which are then tracked through two solar cycles. Additionally, we develop an edge-detection algorithm that we use on SDO/AIA data of a polar hole extension with an approximately linear boundary. These locations are used later to inform part of the helioseismic investigation; (2) develop a local time-distance (TD) helioseismology technique that can be used to detect CH boundary signatures at the photospheric level. We employ a new averaging scheme that makes use of the quasi-linear topology of elongated scattering regions, and create simulated data to test the new technique and compare results of some associated assumptions. This method enhances the wave propagation signal in the direction perpendicular to the linear feature and reduces the computational time of the TD analysis. We also apply a new statistical analysis of the significance of differences between the TD results; and (3) apply the TD techniques to solar CH data from SDO/HMI. The data correspond to the AIA data used in the edge-detection algorithm on EUV images. We look for statistically significant differences between the TD results inside and outside the CH region. In investigation (1), we found that the polar CH areas did not change significantly between minima, even though the magnetic field strength weakened. The results of (2) indicate that TD helioseismology techniques can be extended to make use of feature symmetry in the domain. The linear technique used here produces results that differ between a linear scattering region and a circular scattering region, shown using the simulated data algorithm. This suggests that using usual TD methods on scattering regions that are radially asymmetric may produce results with signatures of the anisotropy. The results of (1) and (3) indicate that the TD signal within our CH is statistically significantly different compared to unrelated quiet sun results. Surprisingly, the TD results in the quiet sun near the CH boundary also show significant differences compared to the separate quiet sun.

Feature Statistics Modulate the Activation of Meaning during Spoken Word Processing

ERIC Educational Resources Information Center

Devereux, Barry J.; Taylor, Kirsten I.; Randall, Billi; Geertzen, Jeroen; Tyler, Lorraine K.

2016-01-01

Understanding spoken words involves a rapid mapping from speech to conceptual representations. One distributed feature-based conceptual account assumes that the statistical characteristics of concepts' features--the number of concepts they occur in ("distinctiveness/sharedness") and likelihood of co-occurrence ("correlational…

Statistical and Measurement Properties of Features Used in Essay Assessment. Research Report. ETS RR-04-21

ERIC Educational Resources Information Center

Haberman, Shelby J.

2004-01-01

Statistical and measurement properties are examined for features used in essay assessment to determine the generalizability of the features across populations, prompts, and individuals. Data are employed from TOEFL® and GMAT® examinations and from writing for Criterion?.

Inclusion of dosimetric data as covariates in toxicity-related radiogenomic studies : A systematic review.

PubMed

Yahya, Noorazrul; Chua, Xin-Jane; Manan, Hanani A; Ismail, Fuad

2018-05-17

This systematic review evaluates the completeness of dosimetric features and their inclusion as covariates in genetic-toxicity association studies. Original research studies associating genetic features and normal tissue complications following radiotherapy were identified from PubMed. The use of dosimetric data was determined by mining the statement of prescription dose, dose fractionation, target volume selection or arrangement and dose distribution. The consideration of the dosimetric data as covariates was based on the statement mentioned in the statistical analysis section. The significance of these covariates was extracted from the results section. Descriptive analyses were performed to determine their completeness and inclusion as covariates. A total of 174 studies were found to satisfy the inclusion criteria. Studies published ≥2010 showed increased use of dose distribution information (p = 0.07). 33% of studies did not include any dose features in the analysis of gene-toxicity associations. Only 29% included dose distribution features as covariates and reported the results. 59% of studies which included dose distribution features found significant associations to toxicity. A large proportion of studies on the correlation of genetic markers with radiotherapy-related side effects considered no dosimetric parameters. Significance of dose distribution features was found in more than half of the studies including these features, emphasizing their importance. Completeness of radiation-specific clinical data may have increased in recent years which may improve gene-toxicity association studies.

Statistical classification approach to discrimination between weak earthquakes and quarry blasts recorded by the Israel Seismic Network

NASA Astrophysics Data System (ADS)

Kushnir, A. F.; Troitsky, E. V.; Haikin, L. M.; Dainty, A.

1999-06-01

A semi-automatic procedure has been developed to achieve statistically optimum discrimination between earthquakes and explosions at local or regional distances based on a learning set specific to a given region. The method is used for step-by-step testing of candidate discrimination features to find the optimum (combination) subset of features, with the decision taken on a rigorous statistical basis. Linear (LDF) and Quadratic (QDF) Discriminant Functions based on Gaussian distributions of the discrimination features are implemented and statistically grounded; the features may be transformed by the Box-Cox transformation z=(1/ α)( yα-1) to make them more Gaussian. Tests of the method were successfully conducted on seismograms from the Israel Seismic Network using features consisting of spectral ratios between and within phases. Results showed that the QDF was more effective than the LDF and required five features out of 18 candidates for the optimum set. It was found that discrimination improved with increasing distance within the local range, and that eliminating transformation of the features and failing to correct for noise led to degradation of discrimination.

Texture Classification by Texton: Statistical versus Binary

PubMed Central

Guo, Zhenhua; Zhang, Zhongcheng; Li, Xiu; Li, Qin; You, Jane

2014-01-01

Using statistical textons for texture classification has shown great success recently. The maximal response 8 (Statistical_MR8), image patch (Statistical_Joint) and locally invariant fractal (Statistical_Fractal) are typical statistical texton algorithms and state-of-the-art texture classification methods. However, there are two limitations when using these methods. First, it needs a training stage to build a texton library, thus the recognition accuracy will be highly depended on the training samples; second, during feature extraction, local feature is assigned to a texton by searching for the nearest texton in the whole library, which is time consuming when the library size is big and the dimension of feature is high. To address the above two issues, in this paper, three binary texton counterpart methods were proposed, Binary_MR8, Binary_Joint, and Binary_Fractal. These methods do not require any training step but encode local feature into binary representation directly. The experimental results on the CUReT, UIUC and KTH-TIPS databases show that binary texton could get sound results with fast feature extraction, especially when the image size is not big and the quality of image is not poor. PMID:24520346

Quantitative topographic differentiation of the neonatal EEG.

PubMed

Paul, Karel; Krajca, Vladimír; Roth, Zdenek; Melichar, Jan; Petránek, Svojmil

2006-09-01

To test the discriminatory topographic potential of a new method of the automatic EEG analysis in neonates. A quantitative description of the neonatal EEG can contribute to the objective assessment of the functional state of the brain, and may improve the precision of diagnosing cerebral dysfunctions manifested by 'disorganization', 'dysrhythmia' or 'dysmaturity'. 21 healthy, full-term newborns were examined polygraphically during sleep (EEG-8 referential derivations, respiration, ECG, EOG, EMG). From each EEG record, two 5-min samples (one from the middle of quiet sleep, the other from the middle of active sleep) were subject to subsequent automatic analysis and were described by 13 variables: spectral features and features describing shape and variability of the signal. The data from individual infants were averaged and the number of variables was reduced by factor analysis. All factors identified by factor analysis were statistically significantly influenced by the location of derivation. A large number of statistically significant differences were also established when comparing the effects of individual derivations on each of the 13 measured variables. Both spectral features and features describing shape and variability of the signal are largely accountable for the topographic differentiation of the neonatal EEG. The presented method of the automatic EEG analysis is capable to assess the topographic characteristics of the neonatal EEG, and it is adequately sensitive and describes the neonatal electroencephalogram with sufficient precision. The discriminatory capability of the used method represents a promise for their application in the clinical practice.

Association of Xerostomia and Ultrasonographic Features of the Major Salivary Glands After Radioactive Iodine Ablation for Papillary Thyroid Carcinoma.

PubMed

Soo Roh, Sang; Wook Kim, Dong; Jin Baek, Hye

2016-11-01

The objective of this study is to evaluate the association between xerostomia and sonographic features of the major salivary glands after patients undergo radioactive iodine ablation (RIA) for papillary thyroid carcinoma (PTC). The study included 256 consecutive patients who underwent total thyroidectomy, RIA, and neck ultrasound examinations. Changes in the ultrasound features of the parotid and submandibular glands after RIA were evaluated retrospectively by a single radiologist, on the basis of direct comparison of sonograms obtained before and after RIA. Clinical data, including the presence of xerostomia, were investigated retrospectively by the same radiologist via a review of the electronic medical records. For 111 of the 256 patients (43.4%), ultrasound examination revealed changes in the major salivary glands after RIA. The presence of xerostomia was undetermined in 85 of the 256 patients. Among the remaining 171 patients, the frequency of xerostomia was 36.8% (63/171). When patients with xerostomia were compared with those without xerostomia, no statistically significant differences in patient sex and age, the dose of RIA received, or the number of RIA sessions were noted (p > 0.05). Considering the changes in the ultrasound features of the major salivary glands after RIA, no statistically significant association was found between xerostomia and the number of involved major salivary glands or the presence of an involved submandibular gland (p > 0.05). In this study, ultrasound was unhelpful for evaluating xerostomia after RIA in patients with PTC.

Dermoscopy of accessory nipples in authors’ own study

PubMed Central

Szymszal, Jan; Silny, Wojciech

2014-01-01

Introduction The accessory nipple (AN) is characterised by its network-like structures, which may suggest the diagnosis of a melanocytic lesion. The knowledge about additional dermoscopic features of AN may greatly minimise the risk of unnecessary surgical excisions. Aim To analyse and present different clinical and dermoscopic forms, in which the AN may appear. Material and methods Ninety AN with dermoscopic features were evaluated in the study, detected in 14 patients between the years 2008 and 2014. Results The most common dermoscopic features of the AN were central, scar-like areas (15/19) and peripheral network-like structures (12/19). A number of cleft-like appearances (8/19) and central network-like structures (7/19) had also been observed. Moreover, among the dermoscopic features, white cobblestone-like structures (7/19), a central round dimpling with a plug (6/19) and fisheye-like structures resembling comedo-like openings (9/19) have all also been noted. There is a statistical significance in the occurrence of white cobblestone-like structures with central network-like structures (Fisher's exact test p = 0.0449). The presence of peripheral network-like structures with the occurrence of central scar-like areas was statistically highly significant (p = 0.0091). The central round dimpling was never observed alongside any central network-like structures in any of the lesions (p = 0.0436). Conclusions Accessory nipples are most commonly characterised by the occurrence of a peripheral network-like structure accompanied by the presence of a scar-like area. PMID:25097482

Identification of natural images and computer-generated graphics based on statistical and textural features.

PubMed

Peng, Fei; Li, Jiao-ting; Long, Min

2015-03-01

To discriminate the acquisition pipelines of digital images, a novel scheme for the identification of natural images and computer-generated graphics is proposed based on statistical and textural features. First, the differences between them are investigated from the view of statistics and texture, and 31 dimensions of feature are acquired for identification. Then, LIBSVM is used for the classification. Finally, the experimental results are presented. The results show that it can achieve an identification accuracy of 97.89% for computer-generated graphics, and an identification accuracy of 97.75% for natural images. The analyses also demonstrate the proposed method has excellent performance, compared with some existing methods based only on statistical features or other features. The method has a great potential to be implemented for the identification of natural images and computer-generated graphics. © 2014 American Academy of Forensic Sciences.

Clinicopathological significance of c-MYC in esophageal squamous cell carcinoma.

PubMed

Lian, Yu; Niu, Xiangdong; Cai, Hui; Yang, Xiaojun; Ma, Haizhong; Ma, Shixun; Zhang, Yupeng; Chen, Yifeng

2017-07-01

Esophageal squamous cell carcinoma is one of the most common malignant tumors. The oncogene c-MYC is thought to be important in the initiation, promotion, and therapy resistance of cancer. In this study, we aim to investigate the clinicopathologic roles of c-MYC in esophageal squamous cell carcinoma tissue. This study is aimed at discovering and analyzing c-MYC expression in a series of human esophageal tissues. A total of 95 esophageal squamous cell carcinoma samples were analyzed by the western blotting and immunohistochemistry techniques. Then, correlation of c-MYC expression with clinicopathological features of esophageal squamous cell carcinoma patients was statistically analyzed. In most esophageal squamous cell carcinoma cases, the c-MYC expression was positive in tumor tissues. The positive rate of c-MYC expression in tumor tissues was 61.05%, obviously higher than the adjacent normal tissues (8.42%, 8/92) and atypical hyperplasia tissues (19.75%, 16/95). There was a statistical difference among adjacent normal tissues, atypical hyperplasia tissues, and tumor tissues. Overexpression of the c-MYC was detected in 61.05% (58/95) esophageal squamous cell carcinomas, which was significantly correlated with the degree of differentiation (p = 0.004). The positive rate of c-MYC expression was 40.0% in well-differentiated esophageal tissues, with a significantly statistical difference (p = 0.004). The positive rate of c-MYC was 41.5% in T1 + T2 esophageal tissues and 74.1% in T3 + T4 esophageal tissues, with a significantly statistical difference (p = 0.001). The positive rate of c-MYC was 45.0% in I + II esophageal tissues and 72.2% in III + IV esophageal tissues, with a significantly statistical difference (p = 0.011). The c-MYC expression strongly correlated with clinical staging (p = 0.011), differentiation degree (p = 0.004), lymph node metastasis (p = 0.003), and invasion depth (p = 0.001) of patients with esophageal squamous cell carcinoma. The c-MYC was differentially expressed in a series of human esophageal tissues, and the aberrant c-MYC expression could be a potential factor in carcinogenesis and progression of esophageal squamous cell carcinoma. There was a statistical signification for c-MYC in esophageal squamous cell carcinoma patients to analyze clinicopathological features. It possibly becomes a new diagnostic indicator of esophageal squamous cell carcinoma.

A Statistical Skull Geometry Model for Children 0-3 Years Old

PubMed Central

Li, Zhigang; Park, Byoung-Keon; Liu, Weiguo; Zhang, Jinhuan; Reed, Matthew P.; Rupp, Jonathan D.; Hoff, Carrie N.; Hu, Jingwen

2015-01-01

Head injury is the leading cause of fatality and long-term disability for children. Pediatric heads change rapidly in both size and shape during growth, especially for children under 3 years old (YO). To accurately assess the head injury risks for children, it is necessary to understand the geometry of the pediatric head and how morphologic features influence injury causation within the 0–3 YO population. In this study, head CT scans from fifty-six 0–3 YO children were used to develop a statistical model of pediatric skull geometry. Geometric features important for injury prediction, including skull size and shape, skull thickness and suture width, along with their variations among the sample population, were quantified through a series of image and statistical analyses. The size and shape of the pediatric skull change significantly with age and head circumference. The skull thickness and suture width vary with age, head circumference and location, which will have important effects on skull stiffness and injury prediction. The statistical geometry model developed in this study can provide a geometrical basis for future development of child anthropomorphic test devices and pediatric head finite element models. PMID:25992998

A statistical skull geometry model for children 0-3 years old.

PubMed

Li, Zhigang; Park, Byoung-Keon; Liu, Weiguo; Zhang, Jinhuan; Reed, Matthew P; Rupp, Jonathan D; Hoff, Carrie N; Hu, Jingwen

2015-01-01

Head injury is the leading cause of fatality and long-term disability for children. Pediatric heads change rapidly in both size and shape during growth, especially for children under 3 years old (YO). To accurately assess the head injury risks for children, it is necessary to understand the geometry of the pediatric head and how morphologic features influence injury causation within the 0-3 YO population. In this study, head CT scans from fifty-six 0-3 YO children were used to develop a statistical model of pediatric skull geometry. Geometric features important for injury prediction, including skull size and shape, skull thickness and suture width, along with their variations among the sample population, were quantified through a series of image and statistical analyses. The size and shape of the pediatric skull change significantly with age and head circumference. The skull thickness and suture width vary with age, head circumference and location, which will have important effects on skull stiffness and injury prediction. The statistical geometry model developed in this study can provide a geometrical basis for future development of child anthropomorphic test devices and pediatric head finite element models.

The value of specific MRI features in the evaluation of suspected placental invasion.

PubMed

Lax, Allison; Prince, Martin R; Mennitt, Kevin W; Schwebach, J Reid; Budorick, Nancy E

2007-01-01

The objective of this study was to determine imaging features that may help predict the presence of placenta accreta, placenta increta or placenta percreta on prenatal MRI scanning. A retrospective review of the prenatal MR scans of 10 patients with a diagnosis of placenta accreta, placenta increta or placenta percreta made by pathologic and clinical reports and of 10 patients without placental invasion was performed. Two expert MRI readers were blinded to the patients' true diagnosis and were asked to score a total of 17 MRI features of the placenta and adjacent structures. The interrater reliability was assessed using kappa statistics. The features with a moderate kappa statistic or better (kappa > .40) were then compared with the true diagnosis for each observer. Seven of the scored features had an interobserver reliability of kappa > .40: placenta previa (kappa = .83); abnormal uterine bulging (kappa = .48); intraplacental hemorrhage (kappa = .51); heterogeneity of signal intensity on T2-weighted (T2W) imaging (kappa = .61); the presence of dark intraplacental bands on T2W imaging (kappa = .53); increased placental thickness (kappa = .69); and visualization of the myometrium beneath the placenta on T2W imaging (kappa = .44). Using Fisher's two-sided exact test, there was a statistically significant difference between the proportion of patients with placental invasion and those without placental invasion for three of the features: abnormal uterine bulging (Rater 1, P = .005; Rater 2, P = .011); heterogeneity of T2W imaging signal intensity (Rater 1, P = .006; Rater 2, P = .010); and presence of dark intraplacental bands on T2W imaging (Rater 1, P = .003; Rater 2, P = .033). MRI can be a useful adjunct to ultrasound in diagnosing placenta accreta prenatally. Three features that are seen on MRI in patients with placental invasion appear to be useful for diagnosis: uterine bulging; heterogeneous signal intensity within the placenta; and the presence of dark intraplacental bands on T2W imaging.

Prediction of body mass index status from voice signals based on machine learning for automated medical applications.

PubMed

Lee, Bum Ju; Kim, Keun Ho; Ku, Boncho; Jang, Jun-Su; Kim, Jong Yeol

2013-05-01

The body mass index (BMI) provides essential medical information related to body weight for the treatment and prognosis prediction of diseases such as cardiovascular disease, diabetes, and stroke. We propose a method for the prediction of normal, overweight, and obese classes based only on the combination of voice features that are associated with BMI status, independently of weight and height measurements. A total of 1568 subjects were divided into 4 groups according to age and gender differences. We performed statistical analyses by analysis of variance (ANOVA) and Scheffe test to find significant features in each group. We predicted BMI status (normal, overweight, and obese) by a logistic regression algorithm and two ensemble classification algorithms (bagging and random forests) based on statistically significant features. In the Female-2030 group (females aged 20-40 years), classification experiments using an imbalanced (original) data set gave area under the receiver operating characteristic curve (AUC) values of 0.569-0.731 by logistic regression, whereas experiments using a balanced data set gave AUC values of 0.893-0.994 by random forests. AUC values in Female-4050 (females aged 41-60 years), Male-2030 (males aged 20-40 years), and Male-4050 (males aged 41-60 years) groups by logistic regression in imbalanced data were 0.585-0.654, 0.581-0.614, and 0.557-0.653, respectively. AUC values in Female-4050, Male-2030, and Male-4050 groups in balanced data were 0.629-0.893 by bagging, 0.707-0.916 by random forests, and 0.695-0.854 by bagging, respectively. In each group, we found discriminatory features showing statistical differences among normal, overweight, and obese classes. The results showed that the classification models built by logistic regression in imbalanced data were better than those built by the other two algorithms, and significant features differed according to age and gender groups. Our results could support the development of BMI diagnosis tools for real-time monitoring; such tools are considered helpful in improving automated BMI status diagnosis in remote healthcare or telemedicine and are expected to have applications in forensic and medical science. Copyright © 2013 Elsevier B.V. All rights reserved.

The dynamics of the Corylus, Alnus, and Betula pollen seasons in the context of climate change (SW Poland).

PubMed

Malkiewicz, Małgorzata; Drzeniecka-Osiadacz, Anetta; Krynicka, Justyna

2016-12-15

The changes in the main features of early spring tree or shrub pollen seasons are important due to the significant impact on the occurrence of pollen-related allergy symptoms. This study shows the results of pollen monitoring for a period of eleven years (2003-2013) using a Burkard volumetric spore trap. The main characteristics of the hazel, alder, and birch pollination season were studied in Wrocław (SW Poland). The statistical analyses do not show a significant trend of annual total pollen count or shift in timing of the pollen season in the period of analysis. The research confirms a great impact (at the statistically significant level of 0.05) of the heat resources on pollination season (the value of the correlation coefficient ranges from -0.63 up to -0.87). Meteorological variables (e.g. sum of temperature for selected period) were compiled to 5-year running means to examine trends. Changes in the pollination period features due to climate change including both timing and intensity of pollen productivity, would have important consequences for allergy sufferers. Copyright © 2016 Elsevier B.V. All rights reserved.

Improved pulmonary nodule classification utilizing quantitative lung parenchyma features.

PubMed

Dilger, Samantha K N; Uthoff, Johanna; Judisch, Alexandra; Hammond, Emily; Mott, Sarah L; Smith, Brian J; Newell, John D; Hoffman, Eric A; Sieren, Jessica C

2015-10-01

Current computer-aided diagnosis (CAD) models for determining pulmonary nodule malignancy characterize nodule shape, density, and border in computed tomography (CT) data. Analyzing the lung parenchyma surrounding the nodule has been minimally explored. We hypothesize that improved nodule classification is achievable by including features quantified from the surrounding lung tissue. To explore this hypothesis, we have developed expanded quantitative CT feature extraction techniques, including volumetric Laws texture energy measures for the parenchyma and nodule, border descriptors using ray-casting and rubber-band straightening, histogram features characterizing densities, and global lung measurements. Using stepwise forward selection and leave-one-case-out cross-validation, a neural network was used for classification. When applied to 50 nodules (22 malignant and 28 benign) from high-resolution CT scans, 52 features (8 nodule, 39 parenchymal, and 5 global) were statistically significant. Nodule-only features yielded an area under the ROC curve of 0.918 (including nodule size) and 0.872 (excluding nodule size). Performance was improved through inclusion of parenchymal (0.938) and global features (0.932). These results show a trend toward increased performance when the parenchyma is included, coupled with the large number of significant parenchymal features that support our hypothesis: the pulmonary parenchyma is influenced differentially by malignant versus benign nodules, assisting CAD-based nodule characterizations.

High-level intuitive features (HLIFs) for intuitive skin lesion description.

PubMed

Amelard, Robert; Glaister, Jeffrey; Wong, Alexander; Clausi, David A

2015-03-01

A set of high-level intuitive features (HLIFs) is proposed to quantitatively describe melanoma in standard camera images. Melanoma is the deadliest form of skin cancer. With rising incidence rates and subjectivity in current clinical detection methods, there is a need for melanoma decision support systems. Feature extraction is a critical step in melanoma decision support systems. Existing feature sets for analyzing standard camera images are comprised of low-level features, which exist in high-dimensional feature spaces and limit the system's ability to convey intuitive diagnostic rationale. The proposed HLIFs were designed to model the ABCD criteria commonly used by dermatologists such that each HLIF represents a human-observable characteristic. As such, intuitive diagnostic rationale can be conveyed to the user. Experimental results show that concatenating the proposed HLIFs with a full low-level feature set increased classification accuracy, and that HLIFs were able to separate the data better than low-level features with statistical significance. An example of a graphical interface for providing intuitive rationale is given.

A Pulsed Thermographic Imaging System for Detection and Identification of Cotton Foreign Matter

PubMed Central

Kuzy, Jesse; Li, Changying

2017-01-01

Detection of foreign matter in cleaned cotton is instrumental to accurately grading cotton quality, which in turn impacts the marketability of the cotton. Current grading systems return estimates of the amount of foreign matter present, but provide no information about the identity of the contaminants. This paper explores the use of pulsed thermographic analysis to detect and identify cotton foreign matter. The design and implementation of a pulsed thermographic analysis system is described. A sample set of 240 foreign matter and cotton lint samples were collected. Hand-crafted waveform features and frequency-domain features were extracted and analyzed for statistical significance. Classification was performed on these features using linear discriminant analysis and support vector machines. Using waveform features and support vector machine classifiers, detection of cotton foreign matter was performed with 99.17% accuracy. Using frequency-domain features and linear discriminant analysis, identification was performed with 90.00% accuracy. These results demonstrate that pulsed thermographic imaging analysis produces data which is of significant utility for the detection and identification of cotton foreign matter. PMID:28273848

A search for spectral lines in gamma-ray bursts using TGRS

NASA Astrophysics Data System (ADS)

Kurczynski, P.; Palmer, D.; Seifert, H.; Teegarden, B. J.; Gehrels, N.; Cline, T. L.; Ramaty, R.; Hurley, K.; Madden, N. W.; Pehl, R. H.

1998-05-01

We present the results of an ongoing search for narrow spectral lines in gamma-ray burst data. TGRS, the Transient Gamma-Ray Spectrometer aboard the Wind satellite is a high energy-resolution Ge device. Thus it is uniquely situated among the array of space-based, burst sensitive instruments to look for line features in gamma-ray burst spectra. Our search strategy adopts a two tiered approach. An automated `quick look' scan searches spectra for statistically significant deviations from the continuum. We analyzed all possible time accumulations of spectra as well as individual spectra for each burst. Follow-up analysis of potential line candidates uses model fitting with F-test and χ2 tests for statistical significance.

Aberrant Expression of Calretinin, D2-40 and Mesothelin in Mucinous and Non-Mucinous Colorectal Carcinomas and Relation to Clinicopathological Features and Prognosis.

PubMed

Foda, Abd AlRahman Mohammad; El-Hawary, Amira Kamal; Hamed, Hazem

2016-10-01

CRC is a heterogeneous disease in terms of morphology, invasive behavior, metastatic capacity, and clinical outcome. Recently, many so-called mesothelial markers, including calretinin, D2-40, WT1, thrombomodulin, mesothelin, and others, have been certified. The aim of this study was to assess the immunohistochemical expression of calretinin and other mesothelial markers (D2-40 and mesothelin) in colorectal mucinous adenocarcinoma (MA) and non mucinous adenocarcinoma (NMA) specimens and relation to clinicopathological features and prognosis using manual tissue microarray technique. We studied tumor tissue specimens from 150 patients with colorectal MA and NMA who underwent radical surgery from January 2007 to January 2012. High-density manual tissue microarrays were constructed using a modified mechanical pencil tip technique, and paraffin sections were submitted for immunohistochemistry using Calretinin, D2-40 and mesothelin expressions. We found that NMA showed significantly more calretinin and D2-40 expression than MA In contrast, no statistically significant difference between NMA and MA was detected in mesothelin expression. There were no statistically significant relations between any of the clinicopathological or histological parameters and any of the three markers. In a univariate analysis, neither calretinin nor D2-40 expressions showed any significant relations to DFS or OS. However, mesothelin luminal expression was significantly associated with worse DFS. Multivariate Cox regression analysis proved that luminal mesothelin expression was an independent negative prognostic factor in NMA. In conclusion, Calretinin, D2-40 and mesothelin are aberrantly expressed in a proportion of CRC cases with more expression in NMA than MA. Aberrant expression of these mesothelial markers was not associated with clinicopathological or histological features of CRCs. Only mesothelin expression appears to be a strong predictor of adverse prognosis.

The effect of feature selection methods on computer-aided detection of masses in mammograms

NASA Astrophysics Data System (ADS)

Hupse, Rianne; Karssemeijer, Nico

2010-05-01

In computer-aided diagnosis (CAD) research, feature selection methods are often used to improve generalization performance of classifiers and shorten computation times. In an application that detects malignant masses in mammograms, we investigated the effect of using a selection criterion that is similar to the final performance measure we are optimizing, namely the mean sensitivity of the system in a predefined range of the free-response receiver operating characteristics (FROC). To obtain the generalization performance of the selected feature subsets, a cross validation procedure was performed on a dataset containing 351 abnormal and 7879 normal regions, each region providing a set of 71 mass features. The same number of noise features, not containing any information, were added to investigate the ability of the feature selection algorithms to distinguish between useful and non-useful features. It was found that significantly higher performances were obtained using feature sets selected by the general test statistic Wilks' lambda than using feature sets selected by the more specific FROC measure. Feature selection leads to better performance when compared to a system in which all features were used.

Comparison of the Cellient(™) automated cell block system and agar cell block method.

PubMed

Kruger, A M; Stevens, M W; Kerley, K J; Carter, C D

2014-12-01

To compare the Cellient(TM) automated cell block system with the agar cell block method in terms of quantity and quality of diagnostic material and morphological, histochemical and immunocytochemical features. Cell blocks were prepared from 100 effusion samples using the agar method and Cellient system, and routinely sectioned and stained for haematoxylin and eosin and periodic acid-Schiff with diastase (PASD). A preliminary immunocytochemical study was performed on selected cases (27/100 cases). Sections were evaluated using a three-point grading system to compare a set of morphological parameters. Statistical analysis was performed using Fisher's exact test. Parameters assessing cellularity, presence of single cells and definition of nuclear membrane, nucleoli, chromatin and cytoplasm showed a statistically significant improvement on Cellient cell blocks compared with agar cell blocks (P < 0.05). No significant difference was seen for definition of cell groups, PASD staining or the intensity or clarity of immunocytochemical staining. A discrepant immunocytochemistry (ICC) result was seen in 21% (13/63) of immunostains. The Cellient technique is comparable with the agar method, with statistically significant results achieved for important morphological features. It demonstrates potential as an alternative cell block preparation method which is relevant for the rapid processing of fine needle aspiration samples, malignant effusions and low-cellularity specimens, where optimal cell morphology and architecture are essential. Further investigation is required to optimize immunocytochemical staining using the Cellient method. © 2014 John Wiley & Sons Ltd.

Quantitative diffusion weighted imaging parameters in tumor and peritumoral stroma for prediction of molecular subtypes in breast cancer

NASA Astrophysics Data System (ADS)

He, Ting; Fan, Ming; Zhang, Peng; Li, Hui; Zhang, Juan; Shao, Guoliang; Li, Lihua

2018-03-01

Breast cancer can be classified into four molecular subtypes of Luminal A, Luminal B, HER2 and Basal-like, which have significant differences in treatment and survival outcomes. We in this study aim to predict immunohistochemistry (IHC) determined molecular subtypes of breast cancer using image features derived from tumor and peritumoral stroma region based on diffusion weighted imaging (DWI). A dataset of 126 breast cancer patients were collected who underwent preoperative breast MRI with a 3T scanner. The apparent diffusion coefficients (ADCs) were recorded from DWI, and breast image was segmented into regions comprising the tumor and the surrounding stromal. Statistical characteristics in various breast tumor and peritumoral regions were computed, including mean, minimum, maximum, variance, interquartile range, range, skewness, and kurtosis of ADC values. Additionally, the difference of features between each two regions were also calculated. The univariate logistic based classifier was performed for evaluating the performance of the individual features for discriminating subtypes. For multi-class classification, multivariate logistic regression model was trained and validated. The results showed that the tumor boundary and proximal peritumoral stroma region derived features have a higher performance in classification compared to that of the other regions. Furthermore, the prediction model using statistical features, difference features and all the features combined from these regions generated AUC values of 0.774, 0.796 and 0.811, respectively. The results in this study indicate that ADC feature in tumor and peritumoral stromal region would be valuable for estimating the molecular subtype in breast cancer.

A New Feature-Enhanced Speckle Reduction Method Based on Multiscale Analysis for Ultrasound B-Mode Imaging.

PubMed

Kang, Jinbum; Lee, Jae Young; Yoo, Yangmo

2016-06-01

Effective speckle reduction in ultrasound B-mode imaging is important for enhancing the image quality and improving the accuracy in image analysis and interpretation. In this paper, a new feature-enhanced speckle reduction (FESR) method based on multiscale analysis and feature enhancement filtering is proposed for ultrasound B-mode imaging. In FESR, clinical features (e.g., boundaries and borders of lesions) are selectively emphasized by edge, coherence, and contrast enhancement filtering from fine to coarse scales while simultaneously suppressing speckle development via robust diffusion filtering. In the simulation study, the proposed FESR method showed statistically significant improvements in edge preservation, mean structure similarity, speckle signal-to-noise ratio, and contrast-to-noise ratio (CNR) compared with other speckle reduction methods, e.g., oriented speckle reducing anisotropic diffusion (OSRAD), nonlinear multiscale wavelet diffusion (NMWD), the Laplacian pyramid-based nonlinear diffusion and shock filter (LPNDSF), and the Bayesian nonlocal means filter (OBNLM). Similarly, the FESR method outperformed the OSRAD, NMWD, LPNDSF, and OBNLM methods in terms of CNR, i.e., 10.70 ± 0.06 versus 9.00 ± 0.06, 9.78 ± 0.06, 8.67 ± 0.04, and 9.22 ± 0.06 in the phantom study, respectively. Reconstructed B-mode images that were developed using the five speckle reduction methods were reviewed by three radiologists for evaluation based on each radiologist's diagnostic preferences. All three radiologists showed a significant preference for the abdominal liver images obtained using the FESR methods in terms of conspicuity, margin sharpness, artificiality, and contrast, p<0.0001. For the kidney and thyroid images, the FESR method showed similar improvement over other methods. However, the FESR method did not show statistically significant improvement compared with the OBNLM method in margin sharpness for the kidney and thyroid images. These results demonstrate that the proposed FESR method can improve the image quality of ultrasound B-mode imaging by enhancing the visualization of lesion features while effectively suppressing speckle noise.

A System-Level Pathway-Phenotype Association Analysis Using Synthetic Feature Random Forest

PubMed Central

Pan, Qinxin; Hu, Ting; Malley, James D.; Andrew, Angeline S.; Karagas, Margaret R.; Moore, Jason H.

2015-01-01

As the cost of genome-wide genotyping decreases, the number of genome-wide association studies (GWAS) has increased considerably. However, the transition from GWAS findings to the underlying biology of various phenotypes remains challenging. As a result, due to its system-level interpretability, pathway analysis has become a popular tool for gaining insights on the underlying biology from high-throughput genetic association data. In pathway analyses, gene sets representing particular biological processes are tested for significant associations with a given phenotype. Most existing pathway analysis approaches rely on single-marker statistics and assume that pathways are independent of each other. As biological systems are driven by complex biomolecular interactions, embracing the complex relationships between single-nucleotide polymorphisms (SNPs) and pathways needs to be addressed. To incorporate the complexity of gene-gene interactions and pathway-pathway relationships, we propose a system-level pathway analysis approach, synthetic feature random forest (SF-RF), which is designed to detect pathway-phenotype associations without making assumptions about the relationships among SNPs or pathways. In our approach, the genotypes of SNPs in a particular pathway are aggregated into a synthetic feature representing that pathway via Random Forest (RF). Multiple synthetic features are analyzed using RF simultaneously and the significance of a synthetic feature indicates the significance of the corresponding pathway. We further complement SF-RF with pathway-based Statistical Epistasis Network (SEN) analysis that evaluates interactions among pathways. By investigating the pathway SEN, we hope to gain additional insights into the genetic mechanisms contributing to the pathway-phenotype association. We apply SF-RF to a population-based genetic study of bladder cancer and further investigate the mechanisms that help explain the pathway-phenotype associations using SEN. The bladder cancer associated pathways we found are both consistent with existing biological knowledge and reveal novel and plausible hypotheses for future biological validations. PMID:24535726

Feature maps driven no-reference image quality prediction of authentically distorted images

NASA Astrophysics Data System (ADS)

Ghadiyaram, Deepti; Bovik, Alan C.

2015-03-01

Current blind image quality prediction models rely on benchmark databases comprised of singly and synthetically distorted images, thereby learning image features that are only adequate to predict human perceived visual quality on such inauthentic distortions. However, real world images often contain complex mixtures of multiple distortions. Rather than a) discounting the effect of these mixtures of distortions on an image's perceptual quality and considering only the dominant distortion or b) using features that are only proven to be efficient for singly distorted images, we deeply study the natural scene statistics of authentically distorted images, in different color spaces and transform domains. We propose a feature-maps-driven statistical approach which avoids any latent assumptions about the type of distortion(s) contained in an image, and focuses instead on modeling the remarkable consistencies in the scene statistics of real world images in the absence of distortions. We design a deep belief network that takes model-based statistical image features derived from a very large database of authentically distorted images as input and discovers good feature representations by generalizing over different distortion types, mixtures, and severities, which are later used to learn a regressor for quality prediction. We demonstrate the remarkable competence of our features for improving automatic perceptual quality prediction on a benchmark database and on the newly designed LIVE Authentic Image Quality Challenge Database and show that our approach of combining robust statistical features and the deep belief network dramatically outperforms the state-of-the-art.

Single Trial EEG Patterns for the Prediction of Individual Differences in Fluid Intelligence.

PubMed

Qazi, Emad-Ul-Haq; Hussain, Muhammad; Aboalsamh, Hatim; Malik, Aamir Saeed; Amin, Hafeez Ullah; Bamatraf, Saeed

2016-01-01

Assessing a person's intelligence level is required in many situations, such as career counseling and clinical applications. EEG evoked potentials in oddball task and fluid intelligence score are correlated because both reflect the cognitive processing and attention. A system for prediction of an individual's fluid intelligence level using single trial Electroencephalography (EEG) signals has been proposed. For this purpose, we employed 2D and 3D contents and 34 subjects each for 2D and 3D, which were divided into low-ability (LA) and high-ability (HA) groups using Raven's Advanced Progressive Matrices (RAPM) test. Using visual oddball cognitive task, neural activity of each group was measured and analyzed over three midline electrodes (Fz, Cz, and Pz). To predict whether an individual belongs to LA or HA group, features were extracted using wavelet decomposition of EEG signals recorded in visual oddball task and support vector machine (SVM) was used as a classifier. Two different types of Haar wavelet transform based features have been extracted from the band (0.3 to 30 Hz) of EEG signals. Statistical wavelet features and wavelet coefficient features from the frequency bands 0.0-1.875 Hz (delta low) and 1.875-3.75 Hz (delta high), resulted in the 100 and 98% prediction accuracies, respectively, both for 2D and 3D contents. The analysis of these frequency bands showed clear difference between LA and HA groups. Further, discriminative values of the features have been validated using statistical significance tests and inter-class and intra-class variation analysis. Also, statistical test showed that there was no effect of 2D and 3D content on the assessment of fluid intelligence level. Comparisons with state-of-the-art techniques showed the superiority of the proposed system.

Association between dermoscopic and reflectance confocal microscopy features of cutaneous melanoma with BRAF mutational status.

PubMed

Bombonato, C; Ribero, S; Pozzobon, F C; Puig-Butille, J A; Badenas, C; Carrera, C; Malvehy, J; Moscarella, E; Lallas, A; Piana, S; Puig, S; Argenziano, G; Longo, C

2017-04-01

Melanomas harbouring common genetic mutations might share certain morphological features detectable with dermoscopy and reflectance confocal microscopy. BRAF mutational status is crucial for the management of metastatic melanoma. To correlate the dermoscopic characteristics of primary cutaneous melanomas with BRAF mutational status. Furthermore, a subset of tumours has also been analysed for the presence of possible confocal features that might be linked with BRAF status. Retrospectively acquired dermoscopic and confocal images of patients with melanoma in tertiary referral academic centres: Skin Cancer Unit in Reggio Emilia and at the Melanoma Unit in Barcelona. Kruskal-Wallis test, logistic regressions, univariate and multivariate analyses have been performed to find dermoscopic and confocal features significantly correlated with BRAF mutational status. Dermoscopically, the presence of irregular peripheral streaks and ulceration were positive predictors of BRAF-mutated melanomas with a statistically significance value, while dotted vessels were more represented in wild-type melanomas. None of the evaluated reflectance confocal microscopy features were correlated with genetic profiling. Ulceration and irregular peripheral streaks represent dermoscopic feature indicative for BRAF-mutated melanoma, while dotted vessels are suggestive for wild-type melanoma. © 2016 European Academy of Dermatology and Venereology.

An exploratory study of clinical measures associated with subsyndromal pathological gambling in patients with binge eating disorder.

PubMed

Yip, Sarah W; White, Marney A; Grilo, Carlos M; Potenza, Marc N

2011-06-01

Both binge eating disorder (BED) and pathological gambling (PG) are characterized by impairments in impulse control. Subsyndromal levels of PG have been associated with measures of adverse health. The nature and significance of PG features in individuals with BED is unknown. Ninety-four patients with BED (28 men and 66 women) were classified by gambling group based on inclusionary criteria for Diagnostic and Statistical Manual-IV (DSM-IV) PG and compared on a range of behavioral, psychological and eating disorder (ED) psychopathology variables. One individual (1.1% of the sample) met criteria for PG, although 18.7% of patients with BED displayed one or more DSM-IV criteria for PG, hereafter referred to as problem gambling features. Men were more likely than women to have problem gambling features. BED patients with problem gambling features were distinguished by lower self-esteem and greater substance problem use. After controlling for gender, findings of reduced self-esteem and increased substance problem use among patients with problem gambling features remained significant. In patients with BED, problem gambling features are associated with a number of heightened clinical problems.

Single trial decoding of belief decision making from EEG and fMRI data using independent components features

PubMed Central

Douglas, Pamela K.; Lau, Edward; Anderson, Ariana; Head, Austin; Kerr, Wesley; Wollner, Margalit; Moyer, Daniel; Li, Wei; Durnhofer, Mike; Bramen, Jennifer; Cohen, Mark S.

2013-01-01

The complex task of assessing the veracity of a statement is thought to activate uniquely distributed brain regions based on whether a subject believes or disbelieves a given assertion. In the current work, we present parallel machine learning methods for predicting a subject's decision response to a given propositional statement based on independent component (IC) features derived from EEG and fMRI data. Our results demonstrate that IC features outperformed features derived from event related spectral perturbations derived from any single spectral band, yet were similar to accuracy across all spectral bands combined. We compared our diagnostic IC spatial maps with our conventional general linear model (GLM) results, and found that informative ICs had significant spatial overlap with our GLM results, yet also revealed unique regions like amygdala that were not statistically significant in GLM analyses. Overall, these results suggest that ICs may yield a parsimonious feature set that can be used along with a decision tree structure for interpretation of features used in classifying complex cognitive processes such as belief and disbelief across both fMRI and EEG neuroimaging modalities. PMID:23914164

Relationship between increasing concentrations of two carcinogens and statistical image descriptors of foci morphology in the cell transformation assay.

PubMed

Callegaro, Giulia; Corvi, Raffaella; Salovaara, Susan; Urani, Chiara; Stefanini, Federico M

2017-06-01

Cell Transformation Assays (CTAs) have long been proposed for the identification of chemical carcinogenicity potential. The endpoint of these in vitro assays is represented by the phenotypic alterations in cultured cells, which are characterized by the change from the non-transformed to the transformed phenotype. Despite the wide fields of application and the numerous advantages of CTAs, their use in regulatory toxicology has been limited in part due to concerns about the subjective nature of visual scoring, i.e. the step in which transformed colonies or foci are evaluated through morphological features. An objective evaluation of morphological features has been previously obtained through automated digital processing of foci images to extract the value of three statistical image descriptors. In this study a further potential of the CTA using BALB/c 3T3 cells is addressed by analysing the effect of increasing concentrations of two known carcinogens, benzo[a]pyrene and NiCl 2 , with different modes of action on foci morphology. The main result of our quantitative evaluation shows that the concentration of the considered carcinogens has an effect on foci morphology that is statistically significant for the mean of two among the three selected descriptors. Statistical significance also corresponds to visual relevance. The statistical analysis of variations in foci morphology due to concentration allowed to quantify morphological changes that can be visually appreciated but not precisely determined. Therefore, it has the potential of providing new quantitative parameters in CTAs, and of exploiting all the information encoded in foci. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Hematology, cytochemistry and ultrastructure of blood cells in fishing cat (Felis viverrina)

PubMed Central

Salakij, Chaleow; Apibal, Suntaree; Narkkong, Nual-Anong

2007-01-01

Hematological, cytochemical and ultrastructural features of blood cells in fishing cat (Felis viverrina) were evaluated using complete blood cell counts with routine and cytochemical blood stains, and scanning and transmission electron microscopy. No statistically significant difference was found in different genders of this animal. Unique features of blood cells in this animal were identified in hematological, cytochemical and ultrastructural studies. This study contributes to broaden hematological resources in wildlife animals and provides a guideline for identification of blood cells in the fishing cat. PMID:17519570

Using Optical Coherence Tomography to Evaluate Skin Sun Damage and Precancer

PubMed Central

Korde, Vrushali R.; Bonnema, Garret T.; Xu, Wei; Krishnamurthy, Chetankumar; Ranger-Moore, James; Saboda, Kathylynn; Slayton, Lisa D.; Salasche, Stuart J.; Warneke, James A.; Alberts, David S.; Barton, Jennifer K.

2008-01-01

Background and Objectives Optical coherence tomography (OCT) is a depth resolved imaging modality that may aid in identifying sun damaged skin and the precancerous condition actinic keratosis (AK). Study Design/Materials and Methods OCT images were acquired of 112 patients at 2 sun protected and 2 sun exposed sites, with a subsequent biopsy. Each site received a dermatological evaluation, a histological diagnosis, and a solar elastosis (SE) score. OCT images were examined visually and statistically analyzed. Results Characteristic OCT image features were identified of sun protected, undiseased, sun damaged, and AK skin. A statistically significant difference (P < 0.0001) between the average attenuation values of skin with minimal and severe solar elastosis was observed. Significant differences (P < 0.0001) were also found between undiseased skin and AK using a gradient analysis. Using image features, AK could be distinguished from undiseased skin with 86% sensitivity and 83% specificity. Conclusion OCT has the potential to guide biopsies and provide non-invasive measures of skin sun damage and disease state, possibly increasing efficiency of chemopreventive agent trials. PMID:17960754

T2-weighted MRI-derived textural features reflect prostate cancer aggressiveness: preliminary results.

PubMed

Nketiah, Gabriel; Elschot, Mattijs; Kim, Eugene; Teruel, Jose R; Scheenen, Tom W; Bathen, Tone F; Selnæs, Kirsten M

2017-07-01

To evaluate the diagnostic relevance of T2-weighted (T2W) MRI-derived textural features relative to quantitative physiological parameters derived from diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI in Gleason score (GS) 3+4 and 4+3 prostate cancers. 3T multiparametric-MRI was performed on 23 prostate cancer patients prior to prostatectomy. Textural features [angular second moment (ASM), contrast, correlation, entropy], apparent diffusion coefficient (ADC), and DCE pharmacokinetic parameters (K trans and V e ) were calculated from index tumours delineated on the T2W, DW, and DCE images, respectively. The association between the textural features and prostatectomy GS and the MRI-derived parameters, and the utility of the parameters in differentiating between GS 3+4 and 4+3 prostate cancers were assessed statistically. ASM and entropy correlated significantly (p < 0.05) with both GS and median ADC. Contrast correlated moderately with median ADC. The textural features correlated insignificantly with K trans and V e . GS 4+3 cancers had significantly lower ASM and higher entropy than 3+4 cancers, but insignificant differences in median ADC, K trans , and V e . The combined texture-MRI parameters yielded higher classification accuracy (91%) than the individual parameter sets. T2W MRI-derived textural features could serve as potential diagnostic markers, sensitive to the pathological differences in prostate cancers. • T2W MRI-derived textural features correlate significantly with Gleason score and ADC. • T2W MRI-derived textural features differentiate Gleason score 3+4 from 4+3 cancers. • T2W image textural features could augment tumour characterization.

WE-E-17A-02: Predictive Modeling of Outcome Following SABR for NSCLC Based On Radiomics of FDG-PET Images

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

Li, R; Aguilera, T; Shultz, D

2014-06-15

Purpose: This study aims to develop predictive models of patient outcome by extracting advanced imaging features (i.e., Radiomics) from FDG-PET images. Methods: We acquired pre-treatment PET scans for 51 stage I NSCLC patients treated with SABR. We calculated 139 quantitative features from each patient PET image, including 5 morphological features, 8 statistical features, 27 texture features, and 100 features from the intensity-volume histogram. Based on the imaging features, we aim to distinguish between 2 risk groups of patients: those with regional failure or distant metastasis versus those without. We investigated 3 pattern classification algorithms: linear discriminant analysis (LDA), naive Bayesmore » (NB), and logistic regression (LR). To avoid the curse of dimensionality, we performed feature selection by first removing redundant features and then applying sequential forward selection using the wrapper approach. To evaluate the predictive performance, we performed 10-fold cross validation with 1000 random splits of the data and calculated the area under the ROC curve (AUC). Results: Feature selection identified 2 texture features (homogeneity and/or wavelet decompositions) for NB and LR, while for LDA SUVmax and one texture feature (correlation) were identified. All 3 classifiers achieved statistically significant improvements over conventional PET imaging metrics such as tumor volume (AUC = 0.668) and SUVmax (AUC = 0.737). Overall, NB achieved the best predictive performance (AUC = 0.806). This also compares favorably with MTV using the best threshold at an SUV of 11.6 (AUC = 0.746). At a sensitivity of 80%, NB achieved 69% specificity, while SUVmax and tumor volume only had 36% and 47% specificity. Conclusion: Through a systematic analysis of advanced PET imaging features, we are able to build models with improved predictive value over conventional imaging metrics. If validated in a large independent cohort, the proposed techniques could potentially aid in identifying patients who might benefit from adjuvant therapy.« less

A random forest model based classification scheme for neonatal amplitude-integrated EEG.

PubMed

Chen, Weiting; Wang, Yu; Cao, Guitao; Chen, Guoqiang; Gu, Qiufang

2014-01-01

Modern medical advances have greatly increased the survival rate of infants, while they remain in the higher risk group for neurological problems later in life. For the infants with encephalopathy or seizures, identification of the extent of brain injury is clinically challenging. Continuous amplitude-integrated electroencephalography (aEEG) monitoring offers a possibility to directly monitor the brain functional state of the newborns over hours, and has seen an increasing application in neonatal intensive care units (NICUs). This paper presents a novel combined feature set of aEEG and applies random forest (RF) method to classify aEEG tracings. To that end, a series of experiments were conducted on 282 aEEG tracing cases (209 normal and 73 abnormal ones). Basic features, statistic features and segmentation features were extracted from both the tracing as a whole and the segmented recordings, and then form a combined feature set. All the features were sent to a classifier afterwards. The significance of feature, the data segmentation, the optimization of RF parameters, and the problem of imbalanced datasets were examined through experiments. Experiments were also done to evaluate the performance of RF on aEEG signal classifying, compared with several other widely used classifiers including SVM-Linear, SVM-RBF, ANN, Decision Tree (DT), Logistic Regression(LR), ML, and LDA. The combined feature set can better characterize aEEG signals, compared with basic features, statistic features and segmentation features respectively. With the combined feature set, the proposed RF-based aEEG classification system achieved a correct rate of 92.52% and a high F1-score of 95.26%. Among all of the seven classifiers examined in our work, the RF method got the highest correct rate, sensitivity, specificity, and F1-score, which means that RF outperforms all of the other classifiers considered here. The results show that the proposed RF-based aEEG classification system with the combined feature set is efficient and helpful to better detect the brain disorders in newborns.

A preliminary evaluation of the validity of binge-eating disorder defining features in a community-based sample.

PubMed

Klein, Kelly M; Forney, K Jean; Keel, Pamela K

2016-05-01

Little empirical attention has been paid to the DSM-5 definition of binge-eating disorder (BED), particularly to the associated features of binge episodes. The present study sought to determine how the associated features and undue influence of weight/shape on self-evaluation contribute to evidence of a clinically significant eating disorder. Secondary analyses were conducted on data (N = 80; 76.3% women, 76.3% Caucasian, ages 18-43) collected through an epidemiological study of eating patterns. Descriptive statistics were used to report the sample prevalence of the features, independently and in combination. Correlations and alpha reliability were employed to examine relationships among associated features, distress regarding bingeing, and clinical diagnosis. Regression models and receiver-operating characteristic (ROC) curves were used to determine the utility of the features for explaining variance in distress. Internal consistency reliability for indicators was low, and several features demonstrated low or nonsignificant associations with distress and diagnosis. Feeling disgusted/depressed/guilty was the only unique predictor of distress (p = 0.001). For the ROC curves, three features was the best threshold for predicting distress. Results support the need to refine the features to ensure better detection of clinically significant eating pathology for research inclusion and treatment of the illness. © 2015 Wiley Periodicals, Inc. (Int J Eat Disord 2016; 49:524-528). © 2015 Wiley Periodicals, Inc.

The value of parsing as feature generation for gene mention recognition

PubMed Central

Smith, Larry H; Wilbur, W John

2009-01-01

We measured the extent to which information surrounding a base noun phrase reflects the presence of a gene name, and evaluated seven different parsers in their ability to provide information for that purpose. Using the GENETAG corpus as a gold standard, we performed machine learning to recognize from its context when a base noun phrase contained a gene name. Starting with the best lexical features, we assessed the gain of adding dependency or dependency-like relations from a full sentence parse. Features derived from parsers improved performance in this partial gene mention recognition task by a small but statistically significant amount. There were virtually no differences between parsers in these experiments. PMID:19345281

Changes in the elasticity of fibroadenoma during the menstrual cycle determined by real-time sonoelastography.

PubMed

Kılıç, Fahrettin; Kayadibi, Yasemin; Kocael, Pinar; Velidedeoglu, Mehmet; Bas, Ahmet; Bakan, Selim; Aydogan, Fatih; Karatas, Adem; Yılmaz, Mehmet Halit

2015-06-01

Shear-wave elastography (SWE) presents quantitative data that thought to represent intrinsic features of the target tissue. Factors affecting the metabolism of the breast parenchyma as well as age, menstrual cycle, hormone levels, pregnancy and lactation, pre-compression artifact during the examination could affect these elastic intrinsic features. Aim of our study is to determine variation of fibroadenoma elasticity during the menstrual cycle (MC) by means of real-time shear-wave elastography (SWE) and identify the optimal time for SWE evaluation. Thirty volunteers (aged 20-40 years) who had biopsy-proven fibroadenoma greater than 1cm in diameter, with regular menstrual cycle and without contraceptive medication underwent SWE (ShearWave on Aixplorer, France) once weekly during MC. Statistical data were processed by using the software Statistical Package for the Social Sciences (SPSS) 19.0. A repeated measures analysis of variance was used for each lesion where the repeated factor was the elastographic measurements (premenstrual, menstrual and postmenstrual). Pillai's trace test was used. Pairwise correlation was calculated using Bonferroni correction. Values of p<0.05 were considered statistically significant. The mean elasticity value of fibroadenomas in mid-cycle was 28.49 ± 12.92 kPa, with the highest value obtained in the third week corresponding to the premenstrual stage (32.98 ± 13.35 kPa) and the lowest value obtained in the first week corresponding to the postmenstrual stage (25.39 ± 10.21 kPa). Differences between the elasticity values of fibroadenomas in premenstrual and postmenstrual periods were statistically significant (p<0.001). There were no significant differences in lesion size between the different phases of the menstrual cycle (p>0.05). In this study, we found that there is significant difference between the elasticity values of fibroadenomas on premenstrual and postmenstrual period. We propose that one week after menstruation would be appropriate time to perform breast SWE. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Impact of obsessive-compulsive disorder comorbidity on the sociodemographic and clinical features of patients with bipolar disorder.

PubMed

Koyuncu, Ahmet; Tükel, Raşit; Ozyildirim, Ilker; Meteris, Handan; Yazici, Olcay

2010-01-01

In this study, our aim is to determine the prevalence rates of obsessive-compulsive disorder (OCD) comorbidity and to assess the impact of OCD comorbidity on the sociodemographic and clinical features of patients with bipolar disorder (BD). Using the Yale-Brown Obsessive Compulsive Scale Symptom Checklist and Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition-IV/Clinical Version on bipolar patients, 2 groups, BD with OCD comorbidity (BD-OCD) and BD without OCD comorbidity, were formed. These groups were compared for sociodemographic and clinical variables. Of 214 patients with BD, 21.9% of them had obsession and/or compulsion symptoms and 16.3% had symptoms at the OCD level. Although there was no statistically significant difference between the frequency of comorbid OCD in BD-I (22/185, 11.9%) and BD-II (3/13, 23.1%) patients, but OCD was found to be significantly high in BD not otherwise specified (10/16, %62.5) patients than BD-I (P < .001) and BD-II (P = .03). Six patients (17.1%) of the BD-OCD group had chronic course (the presence of at least 1 mood disorder episode with a duration of longer than 2 years), whereas the BD without OCD group had none, which was statistically significant. There were no statistically significant differences between BD-OCD and BD without OCD groups in terms of age, sex, education, marital status, polarity, age of BD onset, presence of psychotic symptoms, presence of rapid cycling, history of suicide attempts, first episode type, and predominant episode type. Main limitation of our study was the assessment of some variables based on retrospective recall. Our study confirms the high comorbidity rates for OCD in BD patients. Future studies that examine the relationship between OCD and BD using a longitudinal design may be helpful in improving our understanding of the mechanism of this association. 2010 Elsevier Inc. All rights reserved.

Testing statistical self-similarity in the topology of river networks

USGS Publications Warehouse

Troutman, Brent M.; Mantilla, Ricardo; Gupta, Vijay K.

2010-01-01

Recent work has demonstrated that the topological properties of real river networks deviate significantly from predictions of Shreve's random model. At the same time the property of mean self-similarity postulated by Tokunaga's model is well supported by data. Recently, a new class of network model called random self-similar networks (RSN) that combines self-similarity and randomness has been introduced to replicate important topological features observed in real river networks. We investigate if the hypothesis of statistical self-similarity in the RSN model is supported by data on a set of 30 basins located across the continental United States that encompass a wide range of hydroclimatic variability. We demonstrate that the generators of the RSN model obey a geometric distribution, and self-similarity holds in a statistical sense in 26 of these 30 basins. The parameters describing the distribution of interior and exterior generators are tested to be statistically different and the difference is shown to produce the well-known Hack's law. The inter-basin variability of RSN parameters is found to be statistically significant. We also test generator dependence on two climatic indices, mean annual precipitation and radiative index of dryness. Some indication of climatic influence on the generators is detected, but this influence is not statistically significant with the sample size available. Finally, two key applications of the RSN model to hydrology and geomorphology are briefly discussed.

The impact of office chair features on lumbar lordosis, intervertebral joint and sacral tilt angles: a radiographic assessment.

PubMed

De Carvalho, Diana; Grondin, Diane; Callaghan, Jack

2017-10-01

The purpose of this study was to determine which office chair feature is better at improving spine posture in sitting. Participants (n = 28) were radiographed in standing, maximum flexion and seated in four chair conditions: control, lumbar support, seat pan tilt and backrest with scapular relief. Measures of lumbar lordosis, intervertebral joint angles and sacral tilt were compared between conditions and sex. Sitting consisted of approximately 70% of maximum range of spine flexion. No differences in lumbar flexion were found between the chair features or control. Significantly more anterior pelvic rotation was found with the lumbar support (p = 0.0028) and seat pan tilt (p < 0.0001). Males had significantly more anterior pelvic rotation and extended intervertebral joint angles through L1-L3 in all conditions (p < 0.0001). No one feature was statistically superior with respect to minimising spine flexion, however, seat pan tilt resulted in significantly improved pelvic posture. Practitioner Summary: Seat pan tilt, and to some extent lumbar supports, appear to improve seated postures. However, sitting, regardless of chair features used, still involves near end range flexion of the spine. This will increase stresses to the spine and could be a potential injury generator during prolonged seated exposures.

Low-Level Contrast Statistics of Natural Images Can Modulate the Frequency of Event-Related Potentials (ERP) in Humans.

PubMed

Ghodrati, Masoud; Ghodousi, Mahrad; Yoonessi, Ali

2016-01-01

Humans are fast and accurate in categorizing complex natural images. It is, however, unclear what features of visual information are exploited by brain to perceive the images with such speed and accuracy. It has been shown that low-level contrast statistics of natural scenes can explain the variance of amplitude of event-related potentials (ERP) in response to rapidly presented images. In this study, we investigated the effect of these statistics on frequency content of ERPs. We recorded ERPs from human subjects, while they viewed natural images each presented for 70 ms. Our results showed that Weibull contrast statistics, as a biologically plausible model, explained the variance of ERPs the best, compared to other image statistics that we assessed. Our time-frequency analysis revealed a significant correlation between these statistics and ERPs' power within theta frequency band (~3-7 Hz). This is interesting, as theta band is believed to be involved in context updating and semantic encoding. This correlation became significant at ~110 ms after stimulus onset, and peaked at 138 ms. Our results show that not only the amplitude but also the frequency of neural responses can be modulated with low-level contrast statistics of natural images and highlights their potential role in scene perception.

Low-Level Contrast Statistics of Natural Images Can Modulate the Frequency of Event-Related Potentials (ERP) in Humans

PubMed Central

Ghodrati, Masoud; Ghodousi, Mahrad; Yoonessi, Ali

2016-01-01

Humans are fast and accurate in categorizing complex natural images. It is, however, unclear what features of visual information are exploited by brain to perceive the images with such speed and accuracy. It has been shown that low-level contrast statistics of natural scenes can explain the variance of amplitude of event-related potentials (ERP) in response to rapidly presented images. In this study, we investigated the effect of these statistics on frequency content of ERPs. We recorded ERPs from human subjects, while they viewed natural images each presented for 70 ms. Our results showed that Weibull contrast statistics, as a biologically plausible model, explained the variance of ERPs the best, compared to other image statistics that we assessed. Our time-frequency analysis revealed a significant correlation between these statistics and ERPs' power within theta frequency band (~3–7 Hz). This is interesting, as theta band is believed to be involved in context updating and semantic encoding. This correlation became significant at ~110 ms after stimulus onset, and peaked at 138 ms. Our results show that not only the amplitude but also the frequency of neural responses can be modulated with low-level contrast statistics of natural images and highlights their potential role in scene perception. PMID:28018197

Correlation of Thermally Induced Pores with Microstructural Features Using High Energy X-rays

NASA Astrophysics Data System (ADS)

Menasche, David B.; Shade, Paul A.; Lind, Jonathan; Li, Shiu Fai; Bernier, Joel V.; Kenesei, Peter; Schuren, Jay C.; Suter, Robert M.

2016-11-01

Combined application of a near-field High Energy Diffraction Microscopy measurement of crystal lattice orientation fields and a tomographic measurement of pore distributions in a sintered nickel-based superalloy sample allows pore locations to be correlated with microstructural features. Measurements were carried out at the Advanced Photon Source beamline 1-ID using an X-ray energy of 65 keV for each of the measurement modes. The nickel superalloy sample was prepared in such a way as to generate significant thermally induced porosity. A three-dimensionally resolved orientation map is directly overlaid with the tomographically determined pore map through a careful registration procedure. The data are shown to reliably reproduce the expected correlations between specific microstructural features (triple lines and quadruple nodes) and pore positions. With the statistics afforded by the 3D data set, we conclude that within statistical limits, pore formation does not depend on the relative orientations of the grains. The experimental procedures and analysis tools illustrated are being applied to a variety of materials problems in which local heterogeneities can affect materials properties.

Prediction of cervical cancer recurrence using textural features extracted from 18F-FDG PET images acquired with different scanners.

PubMed

Reuzé, Sylvain; Orlhac, Fanny; Chargari, Cyrus; Nioche, Christophe; Limkin, Elaine; Riet, François; Escande, Alexandre; Haie-Meder, Christine; Dercle, Laurent; Gouy, Sébastien; Buvat, Irène; Deutsch, Eric; Robert, Charlotte

2017-06-27

To identify an imaging signature predicting local recurrence for locally advanced cervical cancer (LACC) treated by chemoradiation and brachytherapy from baseline 18F-FDG PET images, and to evaluate the possibility of gathering images from two different PET scanners in a radiomic study. 118 patients were included retrospectively. Two groups (G1, G2) were defined according to the PET scanner used for image acquisition. Eleven radiomic features were extracted from delineated cervical tumors to evaluate: (i) the predictive value of features for local recurrence of LACC, (ii) their reproducibility as a function of the scanner within a hepatic reference volume, (iii) the impact of voxel size on feature values. Eight features were statistically significant predictors of local recurrence in G1 (p < 0.05). The multivariate signature trained in G2 was validated in G1 (AUC=0.76, p<0.001) and identified local recurrence more accurately than SUVmax (p=0.022). Four features were significantly different between G1 and G2 in the liver. Spatial resampling was not sufficient to explain the stratification effect. This study showed that radiomic features could predict local recurrence of LACC better than SUVmax. Further investigation is needed before applying a model designed using data from one PET scanner to another.

Feature-Based Statistical Analysis of Combustion Simulation Data

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

Bennett, J; Krishnamoorthy, V; Liu, S

2011-11-18

We present a new framework for feature-based statistical analysis of large-scale scientific data and demonstrate its effectiveness by analyzing features from Direct Numerical Simulations (DNS) of turbulent combustion. Turbulent flows are ubiquitous and account for transport and mixing processes in combustion, astrophysics, fusion, and climate modeling among other disciplines. They are also characterized by coherent structure or organized motion, i.e. nonlocal entities whose geometrical features can directly impact molecular mixing and reactive processes. While traditional multi-point statistics provide correlative information, they lack nonlocal structural information, and hence, fail to provide mechanistic causality information between organized fluid motion and mixing andmore » reactive processes. Hence, it is of great interest to capture and track flow features and their statistics together with their correlation with relevant scalar quantities, e.g. temperature or species concentrations. In our approach we encode the set of all possible flow features by pre-computing merge trees augmented with attributes, such as statistical moments of various scalar fields, e.g. temperature, as well as length-scales computed via spectral analysis. The computation is performed in an efficient streaming manner in a pre-processing step and results in a collection of meta-data that is orders of magnitude smaller than the original simulation data. This meta-data is sufficient to support a fully flexible and interactive analysis of the features, allowing for arbitrary thresholds, providing per-feature statistics, and creating various global diagnostics such as Cumulative Density Functions (CDFs), histograms, or time-series. We combine the analysis with a rendering of the features in a linked-view browser that enables scientists to interactively explore, visualize, and analyze the equivalent of one terabyte of simulation data. We highlight the utility of this new framework for combustion science; however, it is applicable to many other science domains.« less

Analysis of wheezes using wavelet higher order spectral features.

PubMed

Taplidou, Styliani A; Hadjileontiadis, Leontios J

2010-07-01

Wheezes are musical breath sounds, which usually imply an existing pulmonary obstruction, such as asthma and chronic obstructive pulmonary disease (COPD). Although many studies have addressed the problem of wheeze detection, a limited number of scientific works has focused in the analysis of wheeze characteristics, and in particular, their time-varying nonlinear characteristics. In this study, an effort is made to reveal and statistically analyze the nonlinear characteristics of wheezes and their evolution over time, as they are reflected in the quadratic phase coupling of their harmonics. To this end, the continuous wavelet transform (CWT) is used in combination with third-order spectra to define the analysis domain, where the nonlinear interactions of the harmonics of wheezes and their time variations are revealed by incorporating instantaneous wavelet bispectrum and bicoherence, which provide with the instantaneous biamplitude and biphase curves. Based on this nonlinear information pool, a set of 23 features is proposed for the nonlinear analysis of wheezes. Two complementary perspectives, i.e., general and detailed, related to average performance and to localities, respectively, were used in the construction of the feature set, in order to embed trends and local behaviors, respectively, seen in the nonlinear interaction of the harmonic elements of wheezes over time. The proposed feature set was evaluated on a dataset of wheezes, acquired from adult patients with diagnosed asthma and COPD from a lung sound database. The statistical evaluation of the feature set revealed discrimination ability between the two pathologies for all data subgroupings. In particular, when the total breathing cycle was examined, all 23 features, but one, showed statistically significant difference between the COPD and asthma pathologies, whereas for the subgroupings of inspiratory and expiratory phases, 18 out of 23 and 22 out of 23 features exhibited discrimination power, respectively. This paves the way for the use of the wavelet higher order spectral features as an input vector to an efficient classifier. Apparently, this would integrate the intrinsic characteristics of wheezes within computerized diagnostic tools toward their more efficient evaluation.

A comparative study of hematological parameters of α and β thalassemias in a high prevalence zone: Saudi Arabia

PubMed Central

Mehdi, Syed Riaz; Al Dahmash, Badr Abdullah

2011-01-01

BACKGROUND AND AIMS: Saudi Arabia falls in the high prevalent zone of αα and β thalassemias. Early screening for the type of thalassemia is essential for further investigations and management. The study was carried out to differentiate the type of thalassemia based on red cell indices and other hematological parameters. MATERIALS AND METHODS: The study was carried out on 991 clinically suspected cases of thalassemias in Riyadh, Saudi Arabia. The hematological parameters were studied on Coulter STKS. Cellulose acetate hemoglobin electrophoresis and high-performance liquid chromatography (HPLC) were performed on all the blood samples. Gene deletion studies were carried out by restriction fragment length polymorphism (RFLP) technique using the restriction endonucleases Bam HI. STATISTICAL ANALYSIS: Statistical analysis was performed on SPSS 11.5 version. RESULTS: The hemoglobin electrophoresis and gene studies revealed that there were 406 (40.96%) and 59 (5.95 %) cases of β thalassemia trait and β thalassemia major respectively including adults and children. 426 cases of various deletion forms of α thalassemias were seen. Microcytosis was a common feature in β thalassemias trait and (-α/-α) and (--/αα) types of α thalassemias. MCH was a more significant distinguishing feature among thalassemias. β thalassemia major and α thalassemia (-α/αα) had almost normal hematological parameters. CONCLUSION: MCV and RBC counts are not statistically significant features for discriminating between α and β thalassemias. There is need for development of a discrimination index to differentiate between α and β thalassemias traits on the lines of discriminatory Indices available for distinguishing β thalassemias trait from iron deficiency anemia. PMID:22345994

Automated diagnosis of congestive heart failure using dual tree complex wavelet transform and statistical features extracted from 2s of ECG signals.

PubMed

Sudarshan, Vidya K; Acharya, U Rajendra; Oh, Shu Lih; Adam, Muhammad; Tan, Jen Hong; Chua, Chua Kuang; Chua, Kok Poo; Tan, Ru San

2017-04-01

Identification of alarming features in the electrocardiogram (ECG) signal is extremely significant for the prediction of congestive heart failure (CHF). ECG signal analysis carried out using computer-aided techniques can speed up the diagnosis process and aid in the proper management of CHF patients. Therefore, in this work, dual tree complex wavelets transform (DTCWT)-based methodology is proposed for an automated identification of ECG signals exhibiting CHF from normal. In the experiment, we have performed a DTCWT on ECG segments of 2s duration up to six levels to obtain the coefficients. From these DTCWT coefficients, statistical features are extracted and ranked using Bhattacharyya, entropy, minimum redundancy maximum relevance (mRMR), receiver-operating characteristics (ROC), Wilcoxon, t-test and reliefF methods. Ranked features are subjected to k-nearest neighbor (KNN) and decision tree (DT) classifiers for automated differentiation of CHF and normal ECG signals. We have achieved 99.86% accuracy, 99.78% sensitivity and 99.94% specificity in the identification of CHF affected ECG signals using 45 features. The proposed method is able to detect CHF patients accurately using only 2s of ECG signal length and hence providing sufficient time for the clinicians to further investigate on the severity of CHF and treatments. Copyright © 2017 Elsevier Ltd. All rights reserved.

Low-Complexity Discriminative Feature Selection From EEG Before and After Short-Term Memory Task.

PubMed

Behzadfar, Neda; Firoozabadi, S Mohammad P; Badie, Kambiz

2016-10-01

A reliable and unobtrusive quantification of changes in cortical activity during short-term memory task can be used to evaluate the efficacy of interfaces and to provide real-time user-state information. In this article, we investigate changes in electroencephalogram signals in short-term memory with respect to the baseline activity. The electroencephalogram signals have been analyzed using 9 linear and nonlinear/dynamic measures. We applied statistical Wilcoxon examination and Davis-Bouldian criterion to select optimal discriminative features. The results show that among the features, the permutation entropy significantly increased in frontal lobe and the occipital second lower alpha band activity decreased during memory task. These 2 features reflect the same mental task; however, their correlation with memory task varies in different intervals. In conclusion, it is suggested that the combination of the 2 features would improve the performance of memory based neurofeedback systems. © EEG and Clinical Neuroscience Society (ECNS) 2016.

The limb movement analysis of rehabilitation exercises using wearable inertial sensors.

PubMed

Bingquan Huang; Giggins, Oonagh; Kechadi, Tahar; Caulfield, Brian

2016-08-01

Due to no supervision of a therapist in home based exercise programs, inertial sensor based feedback systems which can accurately assess movement repetitions are urgently required. The synchronicity and the degrees of freedom both show that one movement might resemble another movement signal which is mixed in with another not precisely defined movement. Therefore, the data and feature selections are important for movement analysis. This paper explores the data and feature selection for the limb movement analysis of rehabilitation exercises. The results highlight that the classification accuracy is very sensitive to the mount location of the sensors. The results show that the use of 2 or 3 sensor units, the combination of acceleration and gyroscope data, and the feature sets combined by the statistical feature set with another type of feature, can significantly improve the classification accuracy rates. The results illustrate that acceleration data is more effective than gyroscope data for most of the movement analysis.

Research of facial feature extraction based on MMC

NASA Astrophysics Data System (ADS)

Xue, Donglin; Zhao, Jiufen; Tang, Qinhong; Shi, Shaokun

2017-07-01

Based on the maximum margin criterion (MMC), a new algorithm of statistically uncorrelated optimal discriminant vectors and a new algorithm of orthogonal optimal discriminant vectors for feature extraction were proposed. The purpose of the maximum margin criterion is to maximize the inter-class scatter while simultaneously minimizing the intra-class scatter after the projection. Compared with original MMC method and principal component analysis (PCA) method, the proposed methods are better in terms of reducing or eliminating the statistically correlation between features and improving recognition rate. The experiment results on Olivetti Research Laboratory (ORL) face database shows that the new feature extraction method of statistically uncorrelated maximum margin criterion (SUMMC) are better in terms of recognition rate and stability. Besides, the relations between maximum margin criterion and Fisher criterion for feature extraction were revealed.

Short-term climate variability and atmospheric teleconnections from satellite-observed outgoing longwave radiation. I Simultaneous relationships. II - Lagged correlations

NASA Technical Reports Server (NTRS)

Lau, K.-M.; Chan, P. H.

1983-01-01

Attention is given to the low-frequency variability of outgoing longwave radiation (OLR) fluctuations, their possible correlations over different parts of the globe, and their relationships with teleconnections obtained from other meteorological parameters, for example, geopotential and temperature fields. Simultaneous relationships with respect to the Southern Oscillation (Namais, 1978; Barnett, 1981) signal and the reference OLR fluctuation over the equatorial central Pacific are investigated. Emphasis is placed on the relative importance of the Southern Oscillation (SO) signal over preferred regions. Using lag cross-correlation statistics, possible lagged relationships between the tropics and midlatitudes and their relationships with the SO are then investigated. Only features that are consistent with present knowledge of the dynamics of the system are emphasized. Certain features which may not meet rigorous statistical significance tests but yet are either expected a priori from independent observations or are predicted from dynamical theories are also explored.

Keywords and Co-Occurrence Patterns in the Voynich Manuscript: An Information-Theoretic Analysis

PubMed Central

Montemurro, Marcelo A.; Zanette, Damián H.

2013-01-01

The Voynich manuscript has remained so far as a mystery for linguists and cryptologists. While the text written on medieval parchment -using an unknown script system- shows basic statistical patterns that bear resemblance to those from real languages, there are features that suggested to some researches that the manuscript was a forgery intended as a hoax. Here we analyse the long-range structure of the manuscript using methods from information theory. We show that the Voynich manuscript presents a complex organization in the distribution of words that is compatible with those found in real language sequences. We are also able to extract some of the most significant semantic word-networks in the text. These results together with some previously known statistical features of the Voynich manuscript, give support to the presence of a genuine message inside the book. PMID:23805215

Constructing networks with correlation maximization methods.

PubMed

Mellor, Joseph C; Wu, Jie; Delisi, Charles

2004-01-01

Problems of inference in systems biology are ideally reduced to formulations which can efficiently represent the features of interest. In the case of predicting gene regulation and pathway networks, an important feature which describes connected genes and proteins is the relationship between active and inactive forms, i.e. between the "on" and "off" states of the components. While not optimal at the limits of resolution, these logical relationships between discrete states can often yield good approximations of the behavior in larger complex systems, where exact representation of measurement relationships may be intractable. We explore techniques for extracting binary state variables from measurement of gene expression, and go on to describe robust measures for statistical significance and information that can be applied to many such types of data. We show how statistical strength and information are equivalent criteria in limiting cases, and demonstrate the application of these measures to simple systems of gene regulation.

Logistic regression analysis of conventional ultrasonography, strain elastosonography, and contrast-enhanced ultrasound characteristics for the differentiation of benign and malignant thyroid nodules

PubMed Central

Deng, Yingyuan; Wang, Tianfu; Chen, Siping; Liu, Weixiang

2017-01-01

The aim of the study is to screen the significant sonographic features by logistic regression analysis and fit a model to diagnose thyroid nodules. A total of 525 pathological thyroid nodules were retrospectively analyzed. All the nodules underwent conventional ultrasonography (US), strain elastosonography (SE), and contrast -enhanced ultrasound (CEUS). Those nodules’ 12 suspicious sonographic features were used to assess thyroid nodules. The significant features of diagnosing thyroid nodules were picked out by logistic regression analysis. All variables that were statistically related to diagnosis of thyroid nodules, at a level of p < 0.05 were embodied in a logistic regression analysis model. The significant features in the logistic regression model of diagnosing thyroid nodules were calcification, suspected cervical lymph node metastasis, hypoenhancement pattern, margin, shape, vascularity, posterior acoustic, echogenicity, and elastography score. According to the results of logistic regression analysis, the formula that could predict whether or not thyroid nodules are malignant was established. The area under the receiver operating curve (ROC) was 0.930 and the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 83.77%, 89.56%, 87.05%, 86.04%, and 87.79% respectively. PMID:29228030

Logistic regression analysis of conventional ultrasonography, strain elastosonography, and contrast-enhanced ultrasound characteristics for the differentiation of benign and malignant thyroid nodules.

PubMed

Pang, Tiantian; Huang, Leidan; Deng, Yingyuan; Wang, Tianfu; Chen, Siping; Gong, Xuehao; Liu, Weixiang

2017-01-01

The aim of the study is to screen the significant sonographic features by logistic regression analysis and fit a model to diagnose thyroid nodules. A total of 525 pathological thyroid nodules were retrospectively analyzed. All the nodules underwent conventional ultrasonography (US), strain elastosonography (SE), and contrast -enhanced ultrasound (CEUS). Those nodules' 12 suspicious sonographic features were used to assess thyroid nodules. The significant features of diagnosing thyroid nodules were picked out by logistic regression analysis. All variables that were statistically related to diagnosis of thyroid nodules, at a level of p < 0.05 were embodied in a logistic regression analysis model. The significant features in the logistic regression model of diagnosing thyroid nodules were calcification, suspected cervical lymph node metastasis, hypoenhancement pattern, margin, shape, vascularity, posterior acoustic, echogenicity, and elastography score. According to the results of logistic regression analysis, the formula that could predict whether or not thyroid nodules are malignant was established. The area under the receiver operating curve (ROC) was 0.930 and the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 83.77%, 89.56%, 87.05%, 86.04%, and 87.79% respectively.

Mounting ground sections of teeth: Cyanoacrylate adhesive versus Canada balsam

PubMed Central

Vangala, Manogna RL; Rudraraju, Amrutha; Subramanyam, RV

2016-01-01

Introduction: Hard tissues can be studied by either decalcification or by preparing ground sections. Various mounting media have been tried and used for ground sections of teeth. However, there are very few studies on the use of cyanoacrylate adhesive as a mounting medium. Aims: The aim of our study was to evaluate the efficacy of cyanoacrylate adhesive (Fevikwik™) as a mounting medium for ground sections of teeth and to compare these ground sections with those mounted with Canada balsam. Materials and Methods: Ground sections were prepared from twenty extracted teeth. Each section was divided into two halves and mounted on one slide, one with cyanoacrylate adhesive (Fevikwik™) and the other with Canada balsam. Scoring for various features in the ground sections was done by two independent observers. Statistical Analysis Used: Statistical analysis using Student's t-test (unpaired) of average scores was performed for each feature observed. Results: No statistically significant difference was found between the two for most of the features. However, cyanoacrylate was found to be better than Canada balsam for observing striae of Retzius (P < 0.0205), enamel lamellae (P < 0.036), dentinal tubules (P < 0.0057), interglobular dentin (P < 0.0001), sclerotic dentin – transmitted light (P < 0.00001), sclerotic dentin – polarized light (P < 0.0002) and Sharpey's fibers (P < 0.0004). Conclusions: This initial study shows that cyanoacrylate is better than Canada balsam for observing certain features of ground sections of teeth. However, it remains to be seen whether it will be useful for studying undecalcified sections of carious teeth and for soft tissue sections. PMID:27194857

An intelligent system based on fuzzy probabilities for medical diagnosis– a study in aphasia diagnosis*

PubMed Central

Moshtagh-Khorasani, Majid; Akbarzadeh-T, Mohammad-R; Jahangiri, Nader; Khoobdel, Mehdi

2009-01-01

BACKGROUND: Aphasia diagnosis is particularly challenging due to the linguistic uncertainty and vagueness, inconsistencies in the definition of aphasic syndromes, large number of measurements with imprecision, natural diversity and subjectivity in test objects as well as in opinions of experts who diagnose the disease. METHODS: Fuzzy probability is proposed here as the basic framework for handling the uncertainties in medical diagnosis and particularly aphasia diagnosis. To efficiently construct this fuzzy probabilistic mapping, statistical analysis is performed that constructs input membership functions as well as determines an effective set of input features. RESULTS: Considering the high sensitivity of performance measures to different distribution of testing/training sets, a statistical t-test of significance is applied to compare fuzzy approach results with NN results as well as author's earlier work using fuzzy logic. The proposed fuzzy probability estimator approach clearly provides better diagnosis for both classes of data sets. Specifically, for the first and second type of fuzzy probability classifiers, i.e. spontaneous speech and comprehensive model, P-values are 2.24E-08 and 0.0059, respectively, strongly rejecting the null hypothesis. CONCLUSIONS: The technique is applied and compared on both comprehensive and spontaneous speech test data for diagnosis of four Aphasia types: Anomic, Broca, Global and Wernicke. Statistical analysis confirms that the proposed approach can significantly improve accuracy using fewer Aphasia features. PMID:21772867

Lack of significant association between type 2 diabetes mellitus with longitudinal change in diurnal salivary cortisol: the multiethnic study of atherosclerosis

PubMed Central

Spanakis, Elias K.; Wang, Xu; Sánchez, Brisa N.; Diez Roux, Ana V.; Needham, Belinda L.; Wand, Gary S.; Seeman, Teresa; Golden, Sherita Hill

2016-01-01

Cross-sectional association has been shown between type 2 diabetes and hypothalamic–pituitary–adrenal (HPA) axis dysregulation; however, the temporality of this association is unknown. Our aim was to determine if type 2 diabetes is associated with longitudinal change in daily cortisol curve features. We hypothesized that the presence of type 2 diabetes may lead to a more blunted and abnormal HPA axis profile over time, suggestive of increased HPA axis dysregulation. This was a longitudinal cohort study, including 580 community-dwelling individuals (mean age 63.7 ± 9.1 years; 52.8 % women) with (n = 90) and without (n = 490) type 2 diabetes who attended two MultiEthnic Study of Atherosclerosis Stress ancillary study exams separated by 6 years. Outcome measures that were collected were wake-up and bedtime cortisol, cortisol awakening response (CAR), total area under the curve (AUC), and early, late, and overall decline slopes. In univariate analyses, wake-up and AUC increased over 6 years more in persons with as compared to those without type 2 diabetes (11 vs. 7 % increase for wake-up and 17 vs. 11 % for AUC). The early decline slope became flatter over time with a greater flattening observed in diabetic compared to non-diabetic individuals (23 vs. 9 % flatter); however, the change was only statistically significant for wake-up cortisol (p-value: 0.03). Over time, while CAR was reduced more, late decline and overall decline became flatter, and bedtime cortisol increased less in those with as compared to those without type 2 diabetes, none of these changes were statistically significant in adjusted models. We did not identify any statistically significant change in cortisol curve features over 6 years by type 2 diabetes status. PMID:26895003

Brain morphological and microstructural features in cryptogenic late-onset temporal lobe epilepsy: a structural and diffusion MRI study.

PubMed

Sone, Daichi; Sato, Noriko; Kimura, Yukio; Watanabe, Yutaka; Okazaki, Mitsutoshi; Matsuda, Hiroshi

2018-06-01

Although epilepsy in the elderly has attracted attention recently, there are few systematic studies of neuroimaging in such patients. In this study, we used structural MRI and diffusion tensor imaging (DTI) to investigate the morphological and microstructural features of the brain in late-onset temporal lobe epilepsy (TLE). We recruited patients with TLE and an age of onset > 50 years (late-TLE group) and age- and sex-matched healthy volunteers (control group). 3-Tesla MRI scans, including 3D T1-weighted images and 15-direction DTI, showed normal findings on visual assessment in both groups. We used Statistical Parametric Mapping 12 (SPM12) for gray and white matter structural normalization and comparison and used Tract-Based Spatial Statistics (TBSS) for fractional anisotropy and mean diffusivity comparisons of DTI. In both methods, p < 0.05 (family-wise error) was considered statistically significant. In total, 30 patients with late-onset TLE (mean ± SD age, 66.8 ± 8.4; mean ± SD age of onset, 63.0 ± 7.6 years) and 40 healthy controls (mean ± SD age, 66.6 ± 8.5 years) were enrolled. The late-onset TLE group showed significant gray matter volume increases in the bilateral amygdala and anterior hippocampus and significantly reduced mean diffusivity in the left temporofrontal lobe, internal capsule, and brainstem. No significant changes were evident in white matter volume or fractional anisotropy. Our findings may reflect some characteristics or mechanisms of cryptogenic TLE in the elderly, such as inflammatory processes.

Unjamming a granular hopper by vibration

NASA Astrophysics Data System (ADS)

Janda, A.; Maza, D.; Garcimartín, A.; Kolb, E.; Lanuza, J.; Clément, E.

2009-07-01

We present an experimental study of the outflow of a hopper continuously vibrated by a piezoelectric device. Outpouring of grains can be achieved for apertures much below the usual jamming limit observed for non-vibrated hoppers. Granular flow persists down to the physical limit of one grain diameter, a limit reached for a finite vibration amplitude. For the smaller orifices, we observe an intermittent regime characterized by alternated periods of flow and blockage. Vibrations do not significantly modify the flow rates both in the continuous and the intermittent regime. The analysis of the statistical features of the flowing regime shows that the flow time significantly increases with the vibration amplitude. However, at low vibration amplitude and small orifice sizes, the jamming time distribution displays an anomalous statistics.

LANDSAT survey of near-shore ice conditions along the Arctic coast of Alaska

NASA Technical Reports Server (NTRS)

Stringer, W. J. (Principal Investigator); Barrett, S. A.

1978-01-01

The author has identified the following significant results. Winter and spring near-shore ice conditions were analyzed for the Beaufort Sea 1973-77, and the Chukchi Sea 1973-76. LANDSAT imagery was utilized to map major ice features related to regional ice morphology. Significant features from individual LANDSAT image maps were combined to yield regional maps of major ice ridge systems for each year of study and maps of flaw lead systems for representative seasons during each year. These regional maps were, in turn, used to prepare seasonal ice morphology maps. These maps showed, in terms of a zonal analysis, regions of statistically uniform ice behavior. The behavioral characteristics of each zone were described in terms of coastal processes and bathymetric configuration.

Multi Texture Analysis of Colorectal Cancer Continuum Using Multispectral Imagery

PubMed Central

Chaddad, Ahmad; Desrosiers, Christian; Bouridane, Ahmed; Toews, Matthew; Hassan, Lama; Tanougast, Camel

2016-01-01

Purpose This paper proposes to characterize the continuum of colorectal cancer (CRC) using multiple texture features extracted from multispectral optical microscopy images. Three types of pathological tissues (PT) are considered: benign hyperplasia, intraepithelial neoplasia and carcinoma. Materials and Methods In the proposed approach, the region of interest containing PT is first extracted from multispectral images using active contour segmentation. This region is then encoded using texture features based on the Laplacian-of-Gaussian (LoG) filter, discrete wavelets (DW) and gray level co-occurrence matrices (GLCM). To assess the significance of textural differences between PT types, a statistical analysis based on the Kruskal-Wallis test is performed. The usefulness of texture features is then evaluated quantitatively in terms of their ability to predict PT types using various classifier models. Results Preliminary results show significant texture differences between PT types, for all texture features (p-value < 0.01). Individually, GLCM texture features outperform LoG and DW features in terms of PT type prediction. However, a higher performance can be achieved by combining all texture features, resulting in a mean classification accuracy of 98.92%, sensitivity of 98.12%, and specificity of 99.67%. Conclusions These results demonstrate the efficiency and effectiveness of combining multiple texture features for characterizing the continuum of CRC and discriminating between pathological tissues in multispectral images. PMID:26901134

Transport on Riemannian manifold for functional connectivity-based classification.

PubMed

Ng, Bernard; Dressler, Martin; Varoquaux, Gaël; Poline, Jean Baptiste; Greicius, Michael; Thirion, Bertrand

2014-01-01

We present a Riemannian approach for classifying fMRI connectivity patterns before and after intervention in longitudinal studies. A fundamental difficulty with using connectivity as features is that covariance matrices live on the positive semi-definite cone, which renders their elements inter-related. The implicit independent feature assumption in most classifier learning algorithms is thus violated. In this paper, we propose a matrix whitening transport for projecting the covariance estimates onto a common tangent space to reduce the statistical dependencies between their elements. We show on real data that our approach provides significantly higher classification accuracy than directly using Pearson's correlation. We further propose a non-parametric scheme for identifying significantly discriminative connections from classifier weights. Using this scheme, a number of neuroanatomically meaningful connections are found, whereas no significant connections are detected with pure permutation testing.

Multivariate analysis, mass balance techniques, and statistical tests as tools in igneous petrology: application to the Sierra de las Cruces volcanic range (Mexican Volcanic Belt).

PubMed

Velasco-Tapia, Fernando

2014-01-01

Magmatic processes have usually been identified and evaluated using qualitative or semiquantitative geochemical or isotopic tools based on a restricted number of variables. However, a more complete and quantitative view could be reached applying multivariate analysis, mass balance techniques, and statistical tests. As an example, in this work a statistical and quantitative scheme is applied to analyze the geochemical features for the Sierra de las Cruces (SC) volcanic range (Mexican Volcanic Belt). In this locality, the volcanic activity (3.7 to 0.5 Ma) was dominantly dacitic, but the presence of spheroidal andesitic enclaves and/or diverse disequilibrium features in majority of lavas confirms the operation of magma mixing/mingling. New discriminant-function-based multidimensional diagrams were used to discriminate tectonic setting. Statistical tests of discordancy and significance were applied to evaluate the influence of the subducting Cocos plate, which seems to be rather negligible for the SC magmas in relation to several major and trace elements. A cluster analysis following Ward's linkage rule was carried out to classify the SC volcanic rocks geochemical groups. Finally, two mass-balance schemes were applied for the quantitative evaluation of the proportion of the end-member components (dacitic and andesitic magmas) in the comingled lavas (binary mixtures).

Spectroscopic Diagnosis of Arsenic Contamination in Agricultural Soils

PubMed Central

Shi, Tiezhu; Liu, Huizeng; Chen, Yiyun; Fei, Teng; Wang, Junjie; Wu, Guofeng

2017-01-01

This study investigated the abilities of pre-processing, feature selection and machine-learning methods for the spectroscopic diagnosis of soil arsenic contamination. The spectral data were pre-processed by using Savitzky-Golay smoothing, first and second derivatives, multiplicative scatter correction, standard normal variate, and mean centering. Principle component analysis (PCA) and the RELIEF algorithm were used to extract spectral features. Machine-learning methods, including random forests (RF), artificial neural network (ANN), radial basis function- and linear function- based support vector machine (RBF- and LF-SVM) were employed for establishing diagnosis models. The model accuracies were evaluated and compared by using overall accuracies (OAs). The statistical significance of the difference between models was evaluated by using McNemar’s test (Z value). The results showed that the OAs varied with the different combinations of pre-processing, feature selection, and classification methods. Feature selection methods could improve the modeling efficiencies and diagnosis accuracies, and RELIEF often outperformed PCA. The optimal models established by RF (OA = 86%), ANN (OA = 89%), RBF- (OA = 89%) and LF-SVM (OA = 87%) had no statistical difference in diagnosis accuracies (Z < 1.96, p < 0.05). These results indicated that it was feasible to diagnose soil arsenic contamination using reflectance spectroscopy. The appropriate combination of multivariate methods was important to improve diagnosis accuracies. PMID:28471412

Feature-based and statistical methods for analyzing the Deepwater Horizon oil spill with AVIRIS imagery

USGS Publications Warehouse

Rand, R.S.; Clark, R.N.; Livo, K.E.

2011-01-01

The Deepwater Horizon oil spill covered a very large geographical area in the Gulf of Mexico creating potentially serious environmental impacts on both marine life and the coastal shorelines. Knowing the oil's areal extent and thickness as well as denoting different categories of the oil's physical state is important for assessing these impacts. High spectral resolution data in hyperspectral imagery (HSI) sensors such as Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) provide a valuable source of information that can be used for analysis by semi-automatic methods for tracking an oil spill's areal extent, oil thickness, and oil categories. However, the spectral behavior of oil in water is inherently a highly non-linear and variable phenomenon that changes depending on oil thickness and oil/water ratios. For certain oil thicknesses there are well-defined absorption features, whereas for very thin films sometimes there are almost no observable features. Feature-based imaging spectroscopy methods are particularly effective at classifying materials that exhibit specific well-defined spectral absorption features. Statistical methods are effective at classifying materials with spectra that exhibit a considerable amount of variability and that do not necessarily exhibit well-defined spectral absorption features. This study investigates feature-based and statistical methods for analyzing oil spills using hyperspectral imagery. The appropriate use of each approach is investigated and a combined feature-based and statistical method is proposed.

A method for automatic feature points extraction of human vertebrae three-dimensional model

NASA Astrophysics Data System (ADS)

Wu, Zhen; Wu, Junsheng

2017-05-01

A method for automatic extraction of the feature points of the human vertebrae three-dimensional model is presented. Firstly, the statistical model of vertebrae feature points is established based on the results of manual vertebrae feature points extraction. Then anatomical axial analysis of the vertebrae model is performed according to the physiological and morphological characteristics of the vertebrae. Using the axial information obtained from the analysis, a projection relationship between the statistical model and the vertebrae model to be extracted is established. According to the projection relationship, the statistical model is matched with the vertebrae model to get the estimated position of the feature point. Finally, by analyzing the curvature in the spherical neighborhood with the estimated position of feature points, the final position of the feature points is obtained. According to the benchmark result on multiple test models, the mean relative errors of feature point positions are less than 5.98%. At more than half of the positions, the error rate is less than 3% and the minimum mean relative error is 0.19%, which verifies the effectiveness of the method.

Value of Ki-67 and computed tomography in the assessment of peripheral lung adenocarcinoma.

PubMed

Chen, Cheng; Zhu, Wei-Dong; Zhang, Xiao-Hui; Zhu, Ye-Han; Huang, Jian-An

2016-01-01

This study was designed to determine whether proliferation antigen Ki-67 and/or a computed tomography (CT) value could be used to evaluate the clinical-pathological features of peripheral lung adenocarcinoma. A total of 116 eligible lung cancer patients were enrolled. Nodule size, lymph node metastasis, differentiation, Ki-67 expression and CT findings were assessed. The relationship between clinic parameters and the CT feature was analysed statistically. The percentage of lesions that had ground-glass opacity or localised air bronchogram was significantly greater in low CT value group (<30, p < 0.05). No significant association was observed between CT value and size in the subgroup with CT value > 0 (p = 0.66). As a proliferative marker of lung cancer, Ki-67 was present in a total of 115 (99.9%) of the 116 evaluable primary lung cancers. There was a statistically significant correlation between the Ki-67 index and CT value (p < 0.05). Compared to CT value, Ki-67 index possessed higher sensitivity to predict the differentiation and lymph node metastasis of peripheral lung adenocarcinoma, adding of CT value would enhance its specificity. Combination of Ki-67 expression and CT value determination was useful for the classification of differentiation and metastatic or proliferative potential of peripheral lung adenocarcinoma.

MR image analytics to characterize upper airway architecture in children with OSAS

NASA Astrophysics Data System (ADS)

Tong, Yubing; Udupa, Jayaram K.; Torigian, Drew A.; Matsumoto, Monica M. S.; Sin, Sanghun; Arens, Raanan

2015-03-01

Mechanisms leading to Obstructive Sleep Apnea Syndrome (OSAS) in obese children are not well understood. We previously analyzed polysomnographic and demographic data to study the anatomical characteristics of the upper airway and body composition in two groups of obese children with and without OSAS, where object volume was evaluated. In this paper, in order to better understand the disease we expand the analysis considering a variety of features that include object-specific features such as size, surface area, sphericity, and image intensity properties of fourteen objects in the vicinity of the upper airway, as well as inter-object relationships such as distance between objects. Our preliminary results indicate several interesting phenomena: volumes and surface areas of adenoid and tonsils increase statistically significantly in OSAS. Standardized T2-weighted MR image intensities differ statistically significantly between the two groups, implying that perhaps intrinsic tissue composition undergoes changes in OSAS. Inter-object distances are significantly different between the two groups for object pairs (skin, oropharynx), (skin, fat pad), (skin, soft palate), (mandible, tongue), (oropharynx, soft palate), (left tonsil, oropharynx), (left tonsil, fat pad) and (left tonsil, right tonsil). We conclude that treatment methods for OSAS such as adenotonsillectomy should respect proportional object size relationships and spatial arrangement of objects as they exist in control subjects.

Insights into the sequence parameters for halophilic adaptation.

PubMed

Nath, Abhigyan

2016-03-01

The sequence parameters for halophilic adaptation are still not fully understood. To understand the molecular basis of protein hypersaline adaptation, a detailed analysis is carried out, and investigated the likely association of protein sequence attributes to halophilic adaptation. A two-stage strategy is implemented, where in the first stage a supervised machine learning classifier is build, giving an overall accuracy of 86 % on stratified tenfold cross validation and 90 % on blind testing set, which are better than the previously reported results. The second stage consists of statistical analysis of sequence features and possible extraction of halophilic molecular signatures. The results of this study showed that, halophilic proteins are characterized by lower average charge, lower K content, and lower S content. A statistically significant preference/avoidance list of sequence parameters is also reported giving insights into the molecular basis of halophilic adaptation. D, Q, E, H, P, T, V are significantly preferred while N, C, I, K, M, F, S are significantly avoided. Among amino acid physicochemical groups, small, polar, charged, acidic and hydrophilic groups are preferred over other groups. The halophilic proteins also showed a preference for higher average flexibility, higher average polarity and avoidance for higher average positive charge, average bulkiness and average hydrophobicity. Some interesting trends observed in dipeptide counts are also reported. Further a systematic statistical comparison is undertaken for gaining insights into the sequence feature distribution in different residue structural states. The current analysis may facilitate the understanding of the mechanism of halophilic adaptation clearer, which can be further used for rational design of halophilic proteins.

Radiomic texture-curvature (RTC) features for precision medicine of patients with rheumatoid arthritis-associated interstitial lung disease

NASA Astrophysics Data System (ADS)

Watari, Chinatsu; Matsuhiro, Mikio; Näppi, Janne J.; Nasirudin, Radin A.; Hironaka, Toru; Kawata, Yoshiki; Niki, Noboru; Yoshida, Hiroyuki

2018-03-01

We investigated the effect of radiomic texture-curvature (RTC) features of lung CT images in the prediction of the overall survival of patients with rheumatoid arthritis-associated interstitial lung disease (RA-ILD). We retrospectively collected 70 RA-ILD patients who underwent thin-section lung CT and serial pulmonary function tests. After the extraction of the lung region, we computed hyper-curvature features that included the principal curvatures, curvedness, bright/dark sheets, cylinders, blobs, and curvature scales for the bronchi and the aerated lungs. We also computed gray-level co-occurrence matrix (GLCM) texture features on the segmented lungs. An elastic-net penalty method was used to select and combine these features with a Cox proportional hazards model for predicting the survival of the patient. Evaluation was performed by use of concordance index (C-index) as a measure of prediction performance. The C-index values of the texture features, hyper-curvature features, and the combination thereof (RTC features) in predicting patient survival was estimated by use of bootstrapping with 2,000 replications, and they were compared with an established clinical prognostic biomarker known as the gender, age, and physiology (GAP) index by means of two-sided t-test. Bootstrap evaluation yielded the following C-index values for the clinical and radiomic features: (a) GAP index: 78.3%; (b) GLCM texture features: 79.6%; (c) hypercurvature features: 80.8%; and (d) RTC features: 86.8%. The RTC features significantly outperformed any of the other predictors (P < 0.001). The Kaplan-Meier survival curves of patients stratified to low- and high-risk groups based on the RTC features showed statistically significant (P < 0.0001) difference. Thus, the RTC features can provide an effective imaging biomarker for predicting the overall survival of patients with RA-ILD.

Assessment of features for automatic CTG analysis based on expert annotation.

PubMed

Chudácek, Vacláv; Spilka, Jirí; Lhotská, Lenka; Janku, Petr; Koucký, Michal; Huptych, Michal; Bursa, Miroslav

2011-01-01

Cardiotocography (CTG) is the monitoring of fetal heart rate (FHR) and uterine contractions (TOCO) since 1960's used routinely by obstetricians to detect fetal hypoxia. The evaluation of the FHR in clinical settings is based on an evaluation of macroscopic morphological features and so far has managed to avoid adopting any achievements from the HRV research field. In this work, most of the ever-used features utilized for FHR characterization, including FIGO, HRV, nonlinear, wavelet, and time and frequency domain features, are investigated and the features are assessed based on their statistical significance in the task of distinguishing the FHR into three FIGO classes. Annotation derived from the panel of experts instead of the commonly utilized pH values was used for evaluation of the features on a large data set (552 records). We conclude the paper by presenting the best uncorrelated features and their individual rank of importance according to the meta-analysis of three different ranking methods. Number of acceleration and deceleration, interval index, as well as Lempel-Ziv complexity and Higuchi's fractal dimension are among the top five features.

Classification of pulmonary pathology from breath sounds using the wavelet packet transform and an extreme learning machine.

PubMed

Palaniappan, Rajkumar; Sundaraj, Kenneth; Sundaraj, Sebastian; Huliraj, N; Revadi, S S

2017-06-08

Auscultation is a medical procedure used for the initial diagnosis and assessment of lung and heart diseases. From this perspective, we propose assessing the performance of the extreme learning machine (ELM) classifiers for the diagnosis of pulmonary pathology using breath sounds. Energy and entropy features were extracted from the breath sound using the wavelet packet transform. The statistical significance of the extracted features was evaluated by one-way analysis of variance (ANOVA). The extracted features were inputted into the ELM classifier. The maximum classification accuracies obtained for the conventional validation (CV) of the energy and entropy features were 97.36% and 98.37%, respectively, whereas the accuracies obtained for the cross validation (CRV) of the energy and entropy features were 96.80% and 97.91%, respectively. In addition, maximum classification accuracies of 98.25% and 99.25% were obtained for the CV and CRV of the ensemble features, respectively. The results indicate that the classification accuracy obtained with the ensemble features was higher than those obtained with the energy and entropy features.

Contextual Interactions in Grating Plaid Configurations Are Explained by Natural Image Statistics and Neural Modeling

PubMed Central

Ernst, Udo A.; Schiffer, Alina; Persike, Malte; Meinhardt, Günter

2016-01-01

Processing natural scenes requires the visual system to integrate local features into global object descriptions. To achieve coherent representations, the human brain uses statistical dependencies to guide weighting of local feature conjunctions. Pairwise interactions among feature detectors in early visual areas may form the early substrate of these local feature bindings. To investigate local interaction structures in visual cortex, we combined psychophysical experiments with computational modeling and natural scene analysis. We first measured contrast thresholds for 2 × 2 grating patch arrangements (plaids), which differed in spatial frequency composition (low, high, or mixed), number of grating patch co-alignments (0, 1, or 2), and inter-patch distances (1° and 2° of visual angle). Contrast thresholds for the different configurations were compared to the prediction of probability summation (PS) among detector families tuned to the four retinal positions. For 1° distance the thresholds for all configurations were larger than predicted by PS, indicating inhibitory interactions. For 2° distance, thresholds were significantly lower compared to PS when the plaids were homogeneous in spatial frequency and orientation, but not when spatial frequencies were mixed or there was at least one misalignment. Next, we constructed a neural population model with horizontal laminar structure, which reproduced the detection thresholds after adaptation of connection weights. Consistent with prior work, contextual interactions were medium-range inhibition and long-range, orientation-specific excitation. However, inclusion of orientation-specific, inhibitory interactions between populations with different spatial frequency preferences were crucial for explaining detection thresholds. Finally, for all plaid configurations we computed their likelihood of occurrence in natural images. The likelihoods turned out to be inversely related to the detection thresholds obtained at larger inter-patch distances. However, likelihoods were almost independent of inter-patch distance, implying that natural image statistics could not explain the crowding-like results at short distances. This failure of natural image statistics to resolve the patch distance modulation of plaid visibility remains a challenge to the approach. PMID:27757076

Textural Analysis and Substrate Classification in the Nearshore Region of Lake Superior Using High-Resolution Multibeam Bathymetry

NASA Astrophysics Data System (ADS)

Dennison, Andrew G.

Classification of the seafloor substrate can be done with a variety of methods. These methods include Visual (dives, drop cameras); mechanical (cores, grab samples); acoustic (statistical analysis of echosounder returns). Acoustic methods offer a more powerful and efficient means of collecting useful information about the bottom type. Due to the nature of an acoustic survey, larger areas can be sampled, and by combining the collected data with visual and mechanical survey methods provide greater confidence in the classification of a mapped region. During a multibeam sonar survey, both bathymetric and backscatter data is collected. It is well documented that the statistical characteristic of a sonar backscatter mosaic is dependent on bottom type. While classifying the bottom-type on the basis on backscatter alone can accurately predict and map bottom-type, i.e a muddy area from a rocky area, it lacks the ability to resolve and capture fine textural details, an important factor in many habitat mapping studies. Statistical processing of high-resolution multibeam data can capture the pertinent details about the bottom-type that are rich in textural information. Further multivariate statistical processing can then isolate characteristic features, and provide the basis for an accurate classification scheme. The development of a new classification method is described here. It is based upon the analysis of textural features in conjunction with ground truth sampling. The processing and classification result of two geologically distinct areas in nearshore regions of Lake Superior; off the Lester River,MN and Amnicon River, WI are presented here, using the Minnesota Supercomputer Institute's Mesabi computing cluster for initial processing. Processed data is then calibrated using ground truth samples to conduct an accuracy assessment of the surveyed areas. From analysis of high-resolution bathymetry data collected at both survey sites is was possible to successfully calculate a series of measures that describe textural information about the lake floor. Further processing suggests that the features calculated capture a significant amount of statistical information about the lake floor terrain as well. Two sources of error, an anomalous heave and refraction error significantly deteriorated the quality of the processed data and resulting validate results. Ground truth samples used to validate the classification methods utilized for both survey sites, however, resulted in accuracy values ranging from 5 -30 percent at the Amnicon River, and between 60-70 percent for the Lester River. The final results suggest that this new processing methodology does adequately capture textural information about the lake floor and does provide an acceptable classification in the absence of significant data quality issues.

How have changes in air bag designs affected frontal crash mortality?

PubMed

Braver, Elisa R; Shardell, Michelle; Teoh, Eric R

2010-07-01

To determine whether front air bag changes have affected occupant protection, frontal crash mortality rates were compared among front outboard occupants in vehicles having certified-advanced air bags (latest generation of air bags) or sled-certified air bags with and without advanced features. Poisson marginal structural models were used to calculate standardized mortality rate ratios (MRRs) for front occupants per registered vehicle. Vehicle age-corrected mortality rates were lower for drivers of vehicles having sled-certified air bags with advanced features than for drivers having sled-certified air bags without advanced features (MRR = 0.88; 95% confidence interval [CI]: 0.81-0.95), including unbelted men and drivers younger than 60. The mortality rate was higher, though not statistically significant, for drivers having certified-advanced air bags compared with sled-certified air bags with advanced features (vehicle age-corrected MRR = 1.13; 95% CI: 0.97-1.32) and significantly higher for belted drivers (MRR = 1.21; 95% CI: 1.04-1.39). Advanced air bag features appeared protective for some occupants. However, increased mortality rates among belted drivers of vehicles having certified-advanced air bags relative to those having sled-certified air bags with advanced features suggest that further study is needed to identify any potential problems with requirements for certification. 2010 Elsevier Inc. All rights reserved.

Distinguishing humans from computers in the game of go: A complex network approach

NASA Astrophysics Data System (ADS)

Coquidé, C.; Georgeot, B.; Giraud, O.

2017-08-01

We compare complex networks built from the game of go and obtained from databases of human-played games with those obtained from computer-played games. Our investigations show that statistical features of the human-based networks and the computer-based networks differ, and that these differences can be statistically significant on a relatively small number of games using specific estimators. We show that the deterministic or stochastic nature of the computer algorithm playing the game can also be distinguished from these quantities. This can be seen as a tool to implement a Turing-like test for go simulators.

Scanning probe recognition microscopy investigation of tissue scaffold properties

PubMed Central

Fan, Yuan; Chen, Qian; Ayres, Virginia M; Baczewski, Andrew D; Udpa, Lalita; Kumar, Shiva

2007-01-01

Scanning probe recognition microscopy is a new scanning probe microscopy technique which enables selective scanning along individual nanofibers within a tissue scaffold. Statistically significant data for multiple properties can be collected by repetitively fine-scanning an identical region of interest. The results of a scanning probe recognition microscopy investigation of the surface roughness and elasticity of a series of tissue scaffolds are presented. Deconvolution and statistical methods were developed and used for data accuracy along curved nanofiber surfaces. Nanofiber features were also independently analyzed using transmission electron microscopy, with results that supported the scanning probe recognition microscopy-based analysis. PMID:18203431

Scanning probe recognition microscopy investigation of tissue scaffold properties.

PubMed

Fan, Yuan; Chen, Qian; Ayres, Virginia M; Baczewski, Andrew D; Udpa, Lalita; Kumar, Shiva

2007-01-01

Scanning probe recognition microscopy is a new scanning probe microscopy technique which enables selective scanning along individual nanofibers within a tissue scaffold. Statistically significant data for multiple properties can be collected by repetitively fine-scanning an identical region of interest. The results of a scanning probe recognition microscopy investigation of the surface roughness and elasticity of a series of tissue scaffolds are presented. Deconvolution and statistical methods were developed and used for data accuracy along curved nanofiber surfaces. Nanofiber features were also independently analyzed using transmission electron microscopy, with results that supported the scanning probe recognition microscopy-based analysis.

A novel comparator featured with input data characteristic

NASA Astrophysics Data System (ADS)

Jiang, Xiaobo; Ye, Desheng; Xu, Xiangmin; Zheng, Shuai

2016-03-01

Two types of low-power asynchronous comparators featured with input data statistical characteristic are proposed in this article. The asynchronous ripple comparator stops comparing at the first unequal bit but delivers the result to the least significant bit. The pre-stop asynchronous comparator can completely stop comparing and obtain results immediately. The proposed and contrastive comparators were implemented in SMIC 0.18 μm process with different bit widths. Simulation shows that the proposed pre-stop asynchronous comparator features the lowest power consumption, shortest average propagation delay and highest area efficiency among the comparators. Data path of low-density parity check decoder using the proposed pre-stop asynchronous comparators are most power efficient compared with other data paths with synthesised, clock gating and bitwise competition logic comparators.

Stacked sparse autoencoder in hyperspectral data classification using spectral-spatial, higher order statistics and multifractal spectrum features

NASA Astrophysics Data System (ADS)

Wan, Xiaoqing; Zhao, Chunhui; Wang, Yanchun; Liu, Wu

2017-11-01

This paper proposes a novel classification paradigm for hyperspectral image (HSI) using feature-level fusion and deep learning-based methodologies. Operation is carried out in three main steps. First, during a pre-processing stage, wave atoms are introduced into bilateral filter to smooth HSI, and this strategy can effectively attenuate noise and restore texture information. Meanwhile, high quality spectral-spatial features can be extracted from HSI by taking geometric closeness and photometric similarity among pixels into consideration simultaneously. Second, higher order statistics techniques are firstly introduced into hyperspectral data classification to characterize the phase correlations of spectral curves. Third, multifractal spectrum features are extracted to characterize the singularities and self-similarities of spectra shapes. To this end, a feature-level fusion is applied to the extracted spectral-spatial features along with higher order statistics and multifractal spectrum features. Finally, stacked sparse autoencoder is utilized to learn more abstract and invariant high-level features from the multiple feature sets, and then random forest classifier is employed to perform supervised fine-tuning and classification. Experimental results on two real hyperspectral data sets demonstrate that the proposed method outperforms some traditional alternatives.

Dose fractionation theorem in 3-D reconstruction (tomography)

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

Glaeser, R.M.

It is commonly assumed that the large number of projections for single-axis tomography precludes its application to most beam-labile specimens. However, Hegerl and Hoppe have pointed out that the total dose required to achieve statistical significance for each voxel of a computed 3-D reconstruction is the same as that required to obtain a single 2-D image of that isolated voxel, at the same level of statistical significance. Thus a statistically significant 3-D image can be computed from statistically insignificant projections, as along as the total dosage that is distributed among these projections is high enough that it would have resultedmore » in a statistically significant projection, if applied to only one image. We have tested this critical theorem by simulating the tomographic reconstruction of a realistic 3-D model created from an electron micrograph. The simulations verify the basic conclusions of high absorption, signal-dependent noise, varying specimen contrast and missing angular range. Furthermore, the simulations demonstrate that individual projections in the series of fractionated-dose images can be aligned by cross-correlation because they contain significant information derived from the summation of features from different depths in the structure. This latter information is generally not useful for structural interpretation prior to 3-D reconstruction, owing to the complexity of most specimens investigated by single-axis tomography. These results, in combination with dose estimates for imaging single voxels and measurements of radiation damage in the electron microscope, demonstrate that it is feasible to use single-axis tomography with soft X-ray microscopy of frozen-hydrated specimens.« less

Characterization of spatiotemporal changes for the classification of dynamic contrast-enhanced magnetic-resonance breast lesions.

PubMed

Milenković, Jana; Hertl, Kristijana; Košir, Andrej; Zibert, Janez; Tasič, Jurij Franc

2013-06-01

The early detection of breast cancer is one of the most important predictors in determining the prognosis for women with malignant tumours. Dynamic contrast-enhanced magnetic-resonance imaging (DCE-MRI) is an important imaging modality for detecting and interpreting the different breast lesions from a time sequence of images and has proved to be a very sensitive modality for breast-cancer diagnosis. However, DCE-MRI exhibits only a moderate specificity, thus leading to a high rate of false positives, resulting in unnecessary biopsies that are stressful and physically painful for the patient and lead to an increase in the cost of treatment. There is a strong medical need for a DCE-MRI computer-aided diagnosis tool that would offer a reliable support to the physician's decision providing a high level of sensitivity and specificity. In our study we investigated the possibility of increasing differentiation between the malignant and the benign lesions with respect to the spatial variation of the temporal enhancements of three parametric maps, i.e., the initial enhancement (IE) map, the post-initial enhancement (PIE) map and the signal enhancement ratio (SER) map, by introducing additional methods along with the grey-level co-occurrence matrix, i.e., a second-order statistical method already applied for quantifying the spatiotemporal variations. We introduced the grey-level run-length matrix and the grey-level difference matrix, representing two additional, second-order statistical methods, and the circular Gabor as a frequency-domain-based method. Each of the additional methods is for the first time applied to the DCE-MRI data to differentiate between the malignant and the benign breast lesions. We applied the least-square minimum-distance classifier (LSMD), logistic regression and least-squares support vector machine (LS-SVM) classifiers on a total of 115 (78 malignant and 37 benign) breast DCE-MRI cases. The performances were evaluated using ten experiments of a ten-fold cross-validation. Our experimental analysis revealed the PIE map, together with the feature subset in which the discriminating ability of the co-occurrence features was increased by adding the newly introduced features, to be the most significant for differentiation between the malignant and the benign lesions. That diagnostic test - the aforementioned combination of parametric map and the feature subset achieved the sensitivity of 0.9193 which is statistically significantly higher compared to other diagnostic tests after ten-experiments of a ten-fold cross-validation and gave a statistically significantly higher specificity of 0.7819 for the fixed 95% sensitivity after the receiver operating characteristic (ROC) curve analysis. Combining the information from all the three parametric maps significantly increased the area under the ROC curve (AUC) of the aforementioned diagnostic test for the LSMD and logistic regression; however, not for the LS-SVM. The LSMD classifier yielded the highest area under the ROC curve when using the combined information, increasing the AUC from 0.9651 to 0.9755. Introducing new features to those of the grey-level co-occurrence matrix significantly increased the differentiation between the malignant and the benign breast lesions, thus resulting in a high sensitivity and improved specificity. Copyright © 2013 Elsevier B.V. All rights reserved.

Exploration of EEG features of Alzheimer's disease using continuous wavelet transform.

PubMed

Ghorbanian, Parham; Devilbiss, David M; Hess, Terry; Bernstein, Allan; Simon, Adam J; Ashrafiuon, Hashem

2015-09-01

We have developed a novel approach to elucidate several discriminating EEG features of Alzheimer's disease. The approach is based on the use of a variety of continuous wavelet transforms, pairwise statistical tests with multiple comparison correction, and several decision tree algorithms, in order to choose the most prominent EEG features from a single sensor. A pilot study was conducted to record EEG signals from Alzheimer's disease (AD) patients and healthy age-matched control (CTL) subjects using a single dry electrode device during several eyes-closed (EC) and eyes-open (EO) resting conditions. We computed the power spectrum distribution properties and wavelet and sample entropy of the wavelet coefficients time series at scale ranges approximately corresponding to the major brain frequency bands. A predictive index was developed using the results from statistical tests and decision tree algorithms to identify the most reliable significant features of the AD patients when compared to healthy controls. The three most dominant features were identified as larger absolute mean power and larger standard deviation of the wavelet scales corresponding to 4-8 Hz (θ) during EO and lower wavelet entropy of the wavelet scales corresponding to 8-12 Hz (α) during EC, respectively. The fourth reliable set of distinguishing features of AD patients was lower relative power of the wavelet scales corresponding to 12-30 Hz (β) followed by lower skewness of the wavelet scales corresponding to 2-4 Hz (upper δ), both during EO. In general, the results indicate slowing and lower complexity of EEG signal in AD patients using a very easy-to-use and convenient single dry electrode device.

Differentiation of Uterine Leiomyosarcoma from Atypical Leiomyoma: Diagnostic Accuracy of Qualitative MR Imaging Features and Feasibility of Texture Analysis.

PubMed

Lakhman, Yulia; Veeraraghavan, Harini; Chaim, Joshua; Feier, Diana; Goldman, Debra A; Moskowitz, Chaya S; Nougaret, Stephanie; Sosa, Ramon E; Vargas, Hebert Alberto; Soslow, Robert A; Abu-Rustum, Nadeem R; Hricak, Hedvig; Sala, Evis

2017-07-01

To investigate whether qualitative magnetic resonance (MR) features can distinguish leiomyosarcoma (LMS) from atypical leiomyoma (ALM) and assess the feasibility of texture analysis (TA). This retrospective study included 41 women (ALM = 22, LMS = 19) imaged with MRI prior to surgery. Two readers (R1, R2) evaluated each lesion for qualitative MR features. Associations between MR features and LMS were evaluated with Fisher's exact test. Accuracy measures were calculated for the four most significant features. TA was performed for 24 patients (ALM = 14, LMS = 10) with uniform imaging following lesion segmentation on axial T2-weighted images. Texture features were pre-selected using Wilcoxon signed-rank test with Bonferroni correction and analyzed with unsupervised clustering to separate LMS from ALM. Four qualitative MR features most strongly associated with LMS were nodular borders, haemorrhage, "T2 dark" area(s), and central unenhanced area(s) (p ≤ 0.0001 each feature/reader). The highest sensitivity [1.00 (95%CI:0.82-1.00)/0.95 (95%CI: 0.74-1.00)] and specificity [0.95 (95%CI:0.77-1.00)/1.00 (95%CI:0.85-1.00)] were achieved for R1/R2, respectively, when a lesion had ≥3 of these four features. Sixteen texture features differed significantly between LMS and ALM (p-values: <0.001-0.036). Unsupervised clustering achieved accuracy of 0.75 (sensitivity: 0.70; specificity: 0.79). Combination of ≥3 qualitative MR features accurately distinguished LMS from ALM. TA was feasible. • Four qualitative MR features demonstrated the strongest statistical association with LMS. • Combination of ≥3 these features could accurately differentiate LMS from ALM. • Texture analysis was a feasible semi-automated approach for lesion categorization.

Self-Repairing of Chinese Science and Engineering Majors in Oral English

ERIC Educational Resources Information Center

Wang, Weiwei; Xu, Xiaoqin

2015-01-01

This study employs corpus analytical tools to carry out a systematic study on Chinese Science and Engineering Majors' (SEMs') use of self-repair in their oral English. The study aims to find out the overall feature of using self-repair by SEMs and to see if there exists statistically significant difference of using self-repair across different…

Characterization of coronary plaque regions in intravascular ultrasound images using a hybrid ensemble classifier.

PubMed

Hwang, Yoo Na; Lee, Ju Hwan; Kim, Ga Young; Shin, Eun Seok; Kim, Sung Min

2018-01-01

The purpose of this study was to propose a hybrid ensemble classifier to characterize coronary plaque regions in intravascular ultrasound (IVUS) images. Pixels were allocated to one of four tissues (fibrous tissue (FT), fibro-fatty tissue (FFT), necrotic core (NC), and dense calcium (DC)) through processes of border segmentation, feature extraction, feature selection, and classification. Grayscale IVUS images and their corresponding virtual histology images were acquired from 11 patients with known or suspected coronary artery disease using 20 MHz catheter. A total of 102 hybrid textural features including first order statistics (FOS), gray level co-occurrence matrix (GLCM), extended gray level run-length matrix (GLRLM), Laws, local binary pattern (LBP), intensity, and discrete wavelet features (DWF) were extracted from IVUS images. To select optimal feature sets, genetic algorithm was implemented. A hybrid ensemble classifier based on histogram and texture information was then used for plaque characterization in this study. The optimal feature set was used as input of this ensemble classifier. After tissue characterization, parameters including sensitivity, specificity, and accuracy were calculated to validate the proposed approach. A ten-fold cross validation approach was used to determine the statistical significance of the proposed method. Our experimental results showed that the proposed method had reliable performance for tissue characterization in IVUS images. The hybrid ensemble classification method outperformed other existing methods by achieving characterization accuracy of 81% for FFT and 75% for NC. In addition, this study showed that Laws features (SSV and SAV) were key indicators for coronary tissue characterization. The proposed method had high clinical applicability for image-based tissue characterization. Copyright © 2017 Elsevier B.V. All rights reserved.

Effects of Vocal Fold Nodules on Glottal Cycle Measurements Derived from High-Speed Videoendoscopy in Children

PubMed Central

2016-01-01

The goal of this study is to quantify the effects of vocal fold nodules on vibratory motion in children using high-speed videoendoscopy. Differences in vibratory motion were evaluated in 20 children with vocal fold nodules (5–11 years) and 20 age and gender matched typically developing children (5–11 years) during sustained phonation at typical pitch and loudness. Normalized kinematic features of vocal fold displacements from the mid-membranous vocal fold point were extracted from the steady-state high-speed video. A total of 12 kinematic features representing spatial and temporal characteristics of vibratory motion were calculated. Average values and standard deviations (cycle-to-cycle variability) of the following kinematic features were computed: normalized peak displacement, normalized average opening velocity, normalized average closing velocity, normalized peak closing velocity, speed quotient, and open quotient. Group differences between children with and without vocal fold nodules were statistically investigated. While a moderate effect size was observed for the spatial feature of speed quotient, and the temporal feature of normalized average closing velocity in children with nodules compared to vocally normal children, none of the features were statistically significant between the groups after Bonferroni correction. The kinematic analysis of the mid-membranous vocal fold displacement revealed that children with nodules primarily differ from typically developing children in closing phase kinematics of the glottal cycle, whereas the opening phase kinematics are similar. Higher speed quotients and similar opening phase velocities suggest greater relative forces are acting on vocal fold in the closing phase. These findings suggest that future large-scale studies should focus on spatial and temporal features related to the closing phase of the glottal cycle for differentiating the kinematics of children with and without vocal fold nodules. PMID:27124157

Phenomenology of manic episodes according to the presence or absence of depressive features as defined in DSM-5: Results from the IMPACT self-reported online survey.

PubMed

Vieta, Eduard; Grunze, Heinz; Azorin, Jean-Michel; Fagiolini, Andrea

2014-03-01

The aim of this study was to describe the phenomenology of mania and depression in bipolar patients experiencing a manic episode with mixed features as defined in the new Diagnostic and Statistical Manual of Mental Disorders (DSM-5). In this multicenter, international on-line survey (the IMPACT study), 700 participants completed a 54-item questionnaire on demographics, diagnosis, symptomatology, communication of the disease, impact on life, and treatment received. Patients with a manic episode with or without DSM-5 criteria for mixed features were compared using descriptive and inferential statistics. Patients with more than 3 depressive symptoms were more likely to have had a delay in diagnosis, more likely to have experienced shorter symptom-free periods, and were characterized by a marked lower prevalence of typical manic manifestations. All questionnaire items exploring depressive symptomatology, including the DSM-5 criteria defining a manic episode as "with mixed features", were significantly overrepresented in the group of patients with depressive symptoms. Anxiety associated with irritability/agitation was also more frequent among patients with mixed features. Retrospective cross-sectional design, sensitive to recall bias. Two of the 6 DSM-5 required criteria for the specifier "with mixed features" were not explored: suicidality and psychomotor retardation. Bipolar disorder patients with at least 3 depressive symptoms during a manic episode self-reported typical symptomatology. Anxiety with irritability/agitation differentiated patients with depressive symptoms during mania from those with "pure" manic episodes. The results support the use of DSM-5 mixed features specifier and its value in research and clinical practice. Copyright © 2014 Elsevier B.V. All rights reserved.

Eruption patterns of the chilean volcanoes Villarrica, Llaima, and Tupungatito

NASA Astrophysics Data System (ADS)

Muñoz, Miguel

1983-09-01

The historical eruption records of three Chilean volcanoes have been subjected to many statistical tests, and none have been found to differ significantly from random, or Poissonian, behaviour. The statistical analysis shows rough conformity with the descriptions determined from the eruption rate functions. It is possible that a constant eruption rate describes the activity of Villarrica; Llaima and Tupungatito present complex eruption rate patterns that appear, however, to have no statistical significance. Questions related to loading and extinction processes and to the existence of shallow secondary magma chambers to which magma is supplied from a deeper system are also addressed. The analysis and the computation of the serial correlation coefficients indicate that the three series may be regarded as stationary renewal processes. None of the test statistics indicates rejection of the Poisson hypothesis at a level less than 5%, but the coefficient of variation for the eruption series at Llaima is significantly different from the value expected for a Poisson process. Also, the estimates of the normalized spectrum of the counting process for the three series suggest a departure from the random model, but the deviations are not found to be significant at the 5% level. Kolmogorov-Smirnov and chi-squared test statistics, applied directly to ascertaining to which probability P the random Poisson model fits the data, indicate that there is significant agreement in the case of Villarrica ( P=0.59) and Tupungatito ( P=0.3). Even though the P-value for Llaima is a marginally significant 0.1 (which is equivalent to rejecting the Poisson model at the 90% confidence level), the series suggests that nonrandom features are possibly present in the eruptive activity of this volcano.

Automatic evaluation of intrapartum fetal heart rate recordings: a comprehensive analysis of useful features.

PubMed

Chudáček, V; Spilka, J; Janků, P; Koucký, M; Lhotská, L; Huptych, M

2011-08-01

Cardiotocography is the monitoring of fetal heart rate (FHR) and uterine contractions (TOCO), used routinely since the 1960s by obstetricians to detect fetal hypoxia. The evaluation of the FHR in clinical settings is based on an evaluation of macroscopic morphological features and so far has managed to avoid adopting any achievements from the HRV research field. In this work, most of the features utilized for FHR characterization, including FIGO, HRV, nonlinear, wavelet, and time and frequency domain features, are investigated and assessed based on their statistical significance in the task of distinguishing the FHR into three FIGO classes. We assess the features on a large data set (552 records) and unlike in other published papers we use three-class expert evaluation of the records instead of the pH values. We conclude the paper by presenting the best uncorrelated features and their individual rank of importance according to the meta-analysis of three different ranking methods. The number of accelerations and decelerations, interval index, as well as Lempel-Ziv complexity and Higuchi's fractal dimension are among the top five features.

Evaluation of a web based informatics system with data mining tools for predicting outcomes with quantitative imaging features in stroke rehabilitation clinical trials

NASA Astrophysics Data System (ADS)

Wang, Ximing; Kim, Bokkyu; Park, Ji Hoon; Wang, Erik; Forsyth, Sydney; Lim, Cody; Ravi, Ragini; Karibyan, Sarkis; Sanchez, Alexander; Liu, Brent

2017-03-01

Quantitative imaging biomarkers are used widely in clinical trials for tracking and evaluation of medical interventions. Previously, we have presented a web based informatics system utilizing quantitative imaging features for predicting outcomes in stroke rehabilitation clinical trials. The system integrates imaging features extraction tools and a web-based statistical analysis tool. The tools include a generalized linear mixed model(GLMM) that can investigate potential significance and correlation based on features extracted from clinical data and quantitative biomarkers. The imaging features extraction tools allow the user to collect imaging features and the GLMM module allows the user to select clinical data and imaging features such as stroke lesion characteristics from the database as regressors and regressands. This paper discusses the application scenario and evaluation results of the system in a stroke rehabilitation clinical trial. The system was utilized to manage clinical data and extract imaging biomarkers including stroke lesion volume, location and ventricle/brain ratio. The GLMM module was validated and the efficiency of data analysis was also evaluated.

Bilateral preictal signature of phase-amplitude coupling in canine epilepsy.

PubMed

Gagliano, Laura; Bou Assi, Elie; Nguyen, Dang K; Rihana, Sandy; Sawan, Mohamad

2018-01-01

Seizure forecasting would improve the quality of life of patients with refractory epilepsy. Although early findings were optimistic, no single feature has been found capable of individually characterizing brain dynamics during transition to seizure. Cross-frequency phase amplitude coupling has been recently proposed as a precursor of seizure activity. This work evaluates the existence of a statistically significant difference in mean phase amplitude coupling distribution between the preictal and interictal states of seizures in dogs with bilaterally implanted intracranial electrodes. Results show a statistically significant change (p<0.05) of phase amplitude coupling during the preictal phase. This change is correlated with the position of implanted electrodes and is more significant within high-gamma frequency bands. These findings highlight the potential benefit of bilateral iEEG analysis and the feasibility of seizure forecasting based on slow modulation of high frequency amplitude. Copyright © 2017 Elsevier B.V. All rights reserved.

Haystack, a web-based tool for metabolomics research

PubMed Central

2014-01-01

Background Liquid chromatography coupled to mass spectrometry (LCMS) has become a widely used technique in metabolomics research for differential profiling, the broad screening of biomolecular constituents across multiple samples to diagnose phenotypic differences and elucidate relevant features. However, a significant limitation in LCMS-based metabolomics is the high-throughput data processing required for robust statistical analysis and data modeling for large numbers of samples with hundreds of unique chemical species. Results To address this problem, we developed Haystack, a web-based tool designed to visualize, parse, filter, and extract significant features from LCMS datasets rapidly and efficiently. Haystack runs in a browser environment with an intuitive graphical user interface that provides both display and data processing options. Total ion chromatograms (TICs) and base peak chromatograms (BPCs) are automatically displayed, along with time-resolved mass spectra and extracted ion chromatograms (EICs) over any mass range. Output files in the common .csv format can be saved for further statistical analysis or customized graphing. Haystack's core function is a flexible binning procedure that converts the mass dimension of the chromatogram into a set of interval variables that can uniquely identify a sample. Binned mass data can be analyzed by exploratory methods such as principal component analysis (PCA) to model class assignment and identify discriminatory features. The validity of this approach is demonstrated by comparison of a dataset from plants grown at two light conditions with manual and automated peak detection methods. Haystack successfully predicted class assignment based on PCA and cluster analysis, and identified discriminatory features based on analysis of EICs of significant bins. Conclusion Haystack, a new online tool for rapid processing and analysis of LCMS-based metabolomics data is described. It offers users a range of data visualization options and supports non-biased differential profiling studies through a unique and flexible binning function that provides an alternative to conventional peak deconvolution analysis methods. PMID:25350247

Haystack, a web-based tool for metabolomics research.

PubMed

Grace, Stephen C; Embry, Stephen; Luo, Heng

2014-01-01

Liquid chromatography coupled to mass spectrometry (LCMS) has become a widely used technique in metabolomics research for differential profiling, the broad screening of biomolecular constituents across multiple samples to diagnose phenotypic differences and elucidate relevant features. However, a significant limitation in LCMS-based metabolomics is the high-throughput data processing required for robust statistical analysis and data modeling for large numbers of samples with hundreds of unique chemical species. To address this problem, we developed Haystack, a web-based tool designed to visualize, parse, filter, and extract significant features from LCMS datasets rapidly and efficiently. Haystack runs in a browser environment with an intuitive graphical user interface that provides both display and data processing options. Total ion chromatograms (TICs) and base peak chromatograms (BPCs) are automatically displayed, along with time-resolved mass spectra and extracted ion chromatograms (EICs) over any mass range. Output files in the common .csv format can be saved for further statistical analysis or customized graphing. Haystack's core function is a flexible binning procedure that converts the mass dimension of the chromatogram into a set of interval variables that can uniquely identify a sample. Binned mass data can be analyzed by exploratory methods such as principal component analysis (PCA) to model class assignment and identify discriminatory features. The validity of this approach is demonstrated by comparison of a dataset from plants grown at two light conditions with manual and automated peak detection methods. Haystack successfully predicted class assignment based on PCA and cluster analysis, and identified discriminatory features based on analysis of EICs of significant bins. Haystack, a new online tool for rapid processing and analysis of LCMS-based metabolomics data is described. It offers users a range of data visualization options and supports non-biased differential profiling studies through a unique and flexible binning function that provides an alternative to conventional peak deconvolution analysis methods.

Comparison of Scrub Typhus Meningitis with Acute Bacterial Meningitis and Tuberculous Meningitis.

PubMed

Kakarlapudi, Svas Raju; Chacko, Anila; Samuel, Prasanna; Verghese, Valsan Philip; Rose, Winsley

2018-01-15

To compare scrub typhus meningitis with bacterial and tuberculous meningitis. Children aged <15 years admitted with meningitis were screened and those who fit criteria for diagnosis of scrub typhus meningitis (n=48), bacterial meningitis (n=44) and tuberculous meningitis (n=31) were included for analysis. Clinical features, investigations and outcomes were compared between the three types of meningitis. Mean age, duration of fever at presentation, presence of headache and, altered sensorium and presence of hepatomegaly/splenomegaly were statistically significantly different between the groups. Scrub typhus had statistically significant thrombocytopenia, shorter hospital stay and a better neurological and mortality outcome. Sub-acute presentation of meningitis in older age group children, and good outcome is associated with scrub typhus when compared to bacterial and tuberculous meningitis.

Lung nodule malignancy classification using only radiologist-quantified image features as inputs to statistical learning algorithms: probing the Lung Image Database Consortium dataset with two statistical learning methods

PubMed Central

Hancock, Matthew C.; Magnan, Jerry F.

2016-01-01

Abstract. In the assessment of nodules in CT scans of the lungs, a number of image-derived features are diagnostically relevant. Currently, many of these features are defined only qualitatively, so they are difficult to quantify from first principles. Nevertheless, these features (through their qualitative definitions and interpretations thereof) are often quantified via a variety of mathematical methods for the purpose of computer-aided diagnosis (CAD). To determine the potential usefulness of quantified diagnostic image features as inputs to a CAD system, we investigate the predictive capability of statistical learning methods for classifying nodule malignancy. We utilize the Lung Image Database Consortium dataset and only employ the radiologist-assigned diagnostic feature values for the lung nodules therein, as well as our derived estimates of the diameter and volume of the nodules from the radiologists’ annotations. We calculate theoretical upper bounds on the classification accuracy that are achievable by an ideal classifier that only uses the radiologist-assigned feature values, and we obtain an accuracy of 85.74 (±1.14)%, which is, on average, 4.43% below the theoretical maximum of 90.17%. The corresponding area-under-the-curve (AUC) score is 0.932 (±0.012), which increases to 0.949 (±0.007) when diameter and volume features are included and has an accuracy of 88.08 (±1.11)%. Our results are comparable to those in the literature that use algorithmically derived image-based features, which supports our hypothesis that lung nodules can be classified as malignant or benign using only quantified, diagnostic image features, and indicates the competitiveness of this approach. We also analyze how the classification accuracy depends on specific features and feature subsets, and we rank the features according to their predictive power, statistically demonstrating the top four to be spiculation, lobulation, subtlety, and calcification. PMID:27990453

Lung nodule malignancy classification using only radiologist-quantified image features as inputs to statistical learning algorithms: probing the Lung Image Database Consortium dataset with two statistical learning methods.

PubMed

Hancock, Matthew C; Magnan, Jerry F

2016-10-01

In the assessment of nodules in CT scans of the lungs, a number of image-derived features are diagnostically relevant. Currently, many of these features are defined only qualitatively, so they are difficult to quantify from first principles. Nevertheless, these features (through their qualitative definitions and interpretations thereof) are often quantified via a variety of mathematical methods for the purpose of computer-aided diagnosis (CAD). To determine the potential usefulness of quantified diagnostic image features as inputs to a CAD system, we investigate the predictive capability of statistical learning methods for classifying nodule malignancy. We utilize the Lung Image Database Consortium dataset and only employ the radiologist-assigned diagnostic feature values for the lung nodules therein, as well as our derived estimates of the diameter and volume of the nodules from the radiologists' annotations. We calculate theoretical upper bounds on the classification accuracy that are achievable by an ideal classifier that only uses the radiologist-assigned feature values, and we obtain an accuracy of 85.74 [Formula: see text], which is, on average, 4.43% below the theoretical maximum of 90.17%. The corresponding area-under-the-curve (AUC) score is 0.932 ([Formula: see text]), which increases to 0.949 ([Formula: see text]) when diameter and volume features are included and has an accuracy of 88.08 [Formula: see text]. Our results are comparable to those in the literature that use algorithmically derived image-based features, which supports our hypothesis that lung nodules can be classified as malignant or benign using only quantified, diagnostic image features, and indicates the competitiveness of this approach. We also analyze how the classification accuracy depends on specific features and feature subsets, and we rank the features according to their predictive power, statistically demonstrating the top four to be spiculation, lobulation, subtlety, and calcification.

Local binary pattern variants-based adaptive texture features analysis for posed and nonposed facial expression recognition

NASA Astrophysics Data System (ADS)

Sultana, Maryam; Bhatti, Naeem; Javed, Sajid; Jung, Soon Ki

2017-09-01

Facial expression recognition (FER) is an important task for various computer vision applications. The task becomes challenging when it requires the detection and encoding of macro- and micropatterns of facial expressions. We present a two-stage texture feature extraction framework based on the local binary pattern (LBP) variants and evaluate its significance in recognizing posed and nonposed facial expressions. We focus on the parametric limitations of the LBP variants and investigate their effects for optimal FER. The size of the local neighborhood is an important parameter of the LBP technique for its extraction in images. To make the LBP adaptive, we exploit the granulometric information of the facial images to find the local neighborhood size for the extraction of center-symmetric LBP (CS-LBP) features. Our two-stage texture representations consist of an LBP variant and the adaptive CS-LBP features. Among the presented two-stage texture feature extractions, the binarized statistical image features and adaptive CS-LBP features were found showing high FER rates. Evaluation of the adaptive texture features shows competitive and higher performance than the nonadaptive features and other state-of-the-art approaches, respectively.

Feature selection gait-based gender classification under different circumstances

NASA Astrophysics Data System (ADS)

Sabir, Azhin; Al-Jawad, Naseer; Jassim, Sabah

2014-05-01

This paper proposes a gender classification based on human gait features and investigates the problem of two variations: clothing (wearing coats) and carrying bag condition as addition to the normal gait sequence. The feature vectors in the proposed system are constructed after applying wavelet transform. Three different sets of feature are proposed in this method. First, Spatio-temporal distance that is dealing with the distance of different parts of the human body (like feet, knees, hand, Human Height and shoulder) during one gait cycle. The second and third feature sets are constructed from approximation and non-approximation coefficient of human body respectively. To extract these two sets of feature we divided the human body into two parts, upper and lower body part, based on the golden ratio proportion. In this paper, we have adopted a statistical method for constructing the feature vector from the above sets. The dimension of the constructed feature vector is reduced based on the Fisher score as a feature selection method to optimize their discriminating significance. Finally k-Nearest Neighbor is applied as a classification method. Experimental results demonstrate that our approach is providing more realistic scenario and relatively better performance compared with the existing approaches.

Enhancing image classification models with multi-modal biomarkers

NASA Astrophysics Data System (ADS)

Caban, Jesus J.; Liao, David; Yao, Jianhua; Mollura, Daniel J.; Gochuico, Bernadette; Yoo, Terry

2011-03-01

Currently, most computer-aided diagnosis (CAD) systems rely on image analysis and statistical models to diagnose, quantify, and monitor the progression of a particular disease. In general, CAD systems have proven to be effective at providing quantitative measurements and assisting physicians during the decision-making process. As the need for more flexible and effective CADs continues to grow, questions about how to enhance their accuracy have surged. In this paper, we show how statistical image models can be augmented with multi-modal physiological values to create more robust, stable, and accurate CAD systems. In particular, this paper demonstrates how highly correlated blood and EKG features can be treated as biomarkers and used to enhance image classification models designed to automatically score subjects with pulmonary fibrosis. In our results, a 3-5% improvement was observed when comparing the accuracy of CADs that use multi-modal biomarkers with those that only used image features. Our results show that lab values such as Erythrocyte Sedimentation Rate and Fibrinogen, as well as EKG measurements such as QRS and I:40, are statistically significant and can provide valuable insights about the severity of the pulmonary fibrosis disease.

On the use of multiple-point statistics to improve groundwater flow modeling in karst aquifers: A case study from the Hydrogeological Experimental Site of Poitiers, France

NASA Astrophysics Data System (ADS)

Le Coz, Mathieu; Bodin, Jacques; Renard, Philippe

2017-02-01

Limestone aquifers often exhibit complex groundwater flow behaviors resulting from depositional heterogeneities and post-lithification fracturing and karstification. In this study, multiple-point statistics (MPS) was applied to reproduce karst features and to improve groundwater flow modeling. For this purpose, MPS realizations were used in a numerical flow model to simulate the responses to pumping test experiments observed at the Hydrogeological Experimental Site of Poitiers, France. The main flow behaviors evident in the field data were simulated, particularly (i) the early-time inflection of the drawdown signal at certain observation wells and (ii) the convex behavior of the drawdown curves at intermediate times. In addition, it was shown that the spatial structure of the karst features at various scales is critical with regard to the propagation of the depletion wave induced by pumping. Indeed, (i) the spatial shape of the cone of depression is significantly affected by the karst proportion in the vicinity of the pumping well, and (ii) early-time inflection of the drawdown signal occurs only at observation wells crossing locally well-developed karst features.

Evolution and selection of river networks: Statics, dynamics, and complexity

PubMed Central

Rinaldo, Andrea; Rigon, Riccardo; Banavar, Jayanth R.; Maritan, Amos; Rodriguez-Iturbe, Ignacio

2014-01-01

Moving from the exact result that drainage network configurations minimizing total energy dissipation are stationary solutions of the general equation describing landscape evolution, we review the static properties and the dynamic origins of the scale-invariant structure of optimal river patterns. Optimal channel networks (OCNs) are feasible optimal configurations of a spanning network mimicking landscape evolution and network selection through imperfect searches for dynamically accessible states. OCNs are spanning loopless configurations, however, only under precise physical requirements that arise under the constraints imposed by river dynamics—every spanning tree is exactly a local minimum of total energy dissipation. It is remarkable that dynamically accessible configurations, the local optima, stabilize into diverse metastable forms that are nevertheless characterized by universal statistical features. Such universal features explain very well the statistics of, and the linkages among, the scaling features measured for fluvial landforms across a broad range of scales regardless of geology, exposed lithology, vegetation, or climate, and differ significantly from those of the ground state, known exactly. Results are provided on the emergence of criticality through adaptative evolution and on the yet-unexplored range of applications of the OCN concept. PMID:24550264

Using Saliency-Weighted Disparity Statistics for Objective Visual Comfort Assessment of Stereoscopic Images

NASA Astrophysics Data System (ADS)

Zhang, Wenlan; Luo, Ting; Jiang, Gangyi; Jiang, Qiuping; Ying, Hongwei; Lu, Jing

2016-06-01

Visual comfort assessment (VCA) for stereoscopic images is a particularly significant yet challenging task in 3D quality of experience research field. Although the subjective assessment given by human observers is known as the most reliable way to evaluate the experienced visual discomfort, it is time-consuming and non-systematic. Therefore, it is of great importance to develop objective VCA approaches that can faithfully predict the degree of visual discomfort as human beings do. In this paper, a novel two-stage objective VCA framework is proposed. The main contribution of this study is that the important visual attention mechanism of human visual system is incorporated for visual comfort-aware feature extraction. Specifically, in the first stage, we first construct an adaptive 3D visual saliency detection model to derive saliency map of a stereoscopic image, and then a set of saliency-weighted disparity statistics are computed and combined to form a single feature vector to represent a stereoscopic image in terms of visual comfort. In the second stage, a high dimensional feature vector is fused into a single visual comfort score by performing random forest algorithm. Experimental results on two benchmark databases confirm the superior performance of the proposed approach.

Patellofemoral morphology is not related to pain using three-dimensional quantitative analysis in an older population: data from the Osteoarthritis Initiative

PubMed Central

Drew, Benjamin T.; Bowes, Michael A.; Redmond, Anthony C.; Dube, Bright; Kingsbury, Sarah R.; Conaghan, Philip G.

2017-01-01

Abstract Objectives Current structural associations of patellofemoral pain (PFP) are based on 2D imaging methodology with inherent measurement uncertainty due to positioning and rotation. This study employed novel technology to create 3D measures of commonly described patellofemoral joint imaging features and compared these features in people with and without PFP in a large cohort. Methods We compared two groups from the Osteoarthritis Initiative: one with localized PFP and pain on stairs, and a control group with no knee pain; both groups had no radiographic OA. MRI bone surfaces were automatically segmented and aligned using active appearance models. We applied t-tests, logistic regression and linear discriminant analysis to compare 13 imaging features (including patella position, trochlear morphology, facet area and tilt) converted into 3D equivalents, and a measure of overall 3D shape. Results One hundred and fifteen knees with PFP (mean age 59.7, BMI 27.5 kg/m2, female 58.2%) and 438 without PFP (mean age 63.6, BMI 26.9 kg/m2, female 52.9%) were included. After correction for multiple testing, no statistically significant differences were found between groups for any of the 3D imaging features or their combinations. A statistically significant discrimination was noted for overall 3D shape between genders, confirming the validity of the 3D measures. Conclusion Challenging current perceptions, no differences in patellofemoral morphology were found between older people with and without PFP using 3D quantitative imaging analysis. Further work is needed to see if these findings are replicated in a younger PFP population. PMID:28968747

Neuropathologic features associated with Alzheimer disease diagnosis

PubMed Central

Grinberg, L.T.; Miller, B.; Kawas, C.; Yaffe, K.

2011-01-01

Objective: To examine whether the association between clinical Alzheimer disease (AD) diagnosis and neuropathology and the precision by which neuropathology differentiates people with clinical AD from those with normal cognition varies by age. Methods: We conducted a cross-sectional analysis of 2,014 older adults (≥70 years at death) from the National Alzheimer's Coordinating Center database with clinical diagnosis of normal cognition (made ≤1 year before death, n = 419) or AD (at ≥65 years, n = 1,595) and a postmortem neuropathologic examination evaluating AD pathology (neurofibrillary tangles, neuritic plaques) and non-AD pathology (diffuse plaques, amyloid angiopathy, Lewy bodies, macrovascular disease, microvascular disease). We used adjusted logistic regression to analyze the relationship between clinical AD diagnosis and neuropathologic features, area under the receiver operating characteristic curve (c statistic) to evaluate how precisely neuropathology differentiates between cognitive diagnoses, and an interaction to identify effect modification by age group. Results: In a model controlling for coexisting neuropathologic features, the relationship between clinical AD diagnosis and neurofibrillary tangles was significantly weaker with increasing age (p < 0.001 for interaction). The aggregate of all neuropathologic features more strongly differentiated people with clinical AD from those without in younger age groups (70–74 years: c statistic, 95% confidence interval: 0.93, 0.89–0.96; 75–84 years: 0.95, 0.87–0.95; ≥85 years: 0.83, 0.80–0.87). Non-AD pathology significantly improved precision of differentiation across all age groups (p < 0.004). Conclusion: Clinical AD diagnosis was more weakly associated with neurofibrillary tangles among the oldest old compared to younger age groups, possibly due to less accurate clinical diagnosis, better neurocompensation, or unaccounted pathology among the oldest old. PMID:22031532

A Novel Weighted Kernel PCA-Based Method for Optimization and Uncertainty Quantification

NASA Astrophysics Data System (ADS)

Thimmisetty, C.; Talbot, C.; Chen, X.; Tong, C. H.

2016-12-01

It has been demonstrated that machine learning methods can be successfully applied to uncertainty quantification for geophysical systems through the use of the adjoint method coupled with kernel PCA-based optimization. In addition, it has been shown through weighted linear PCA how optimization with respect to both observation weights and feature space control variables can accelerate convergence of such methods. Linear machine learning methods, however, are inherently limited in their ability to represent features of non-Gaussian stochastic random fields, as they are based on only the first two statistical moments of the original data. Nonlinear spatial relationships and multipoint statistics leading to the tortuosity characteristic of channelized media, for example, are captured only to a limited extent by linear PCA. With the aim of coupling the kernel-based and weighted methods discussed, we present a novel mathematical formulation of kernel PCA, Weighted Kernel Principal Component Analysis (WKPCA), that both captures nonlinear relationships and incorporates the attribution of significance levels to different realizations of the stochastic random field of interest. We also demonstrate how new instantiations retaining defining characteristics of the random field can be generated using Bayesian methods. In particular, we present a novel WKPCA-based optimization method that minimizes a given objective function with respect to both feature space random variables and observation weights through which optimal snapshot significance levels and optimal features are learned. We showcase how WKPCA can be applied to nonlinear optimal control problems involving channelized media, and in particular demonstrate an application of the method to learning the spatial distribution of material parameter values in the context of linear elasticity, and discuss further extensions of the method to stochastic inversion.

Patellofemoral morphology is not related to pain using three-dimensional quantitative analysis in an older population: data from the Osteoarthritis Initiative.

PubMed

Drew, Benjamin T; Bowes, Michael A; Redmond, Anthony C; Dube, Bright; Kingsbury, Sarah R; Conaghan, Philip G

2017-12-01

Current structural associations of patellofemoral pain (PFP) are based on 2D imaging methodology with inherent measurement uncertainty due to positioning and rotation. This study employed novel technology to create 3D measures of commonly described patellofemoral joint imaging features and compared these features in people with and without PFP in a large cohort. We compared two groups from the Osteoarthritis Initiative: one with localized PFP and pain on stairs, and a control group with no knee pain; both groups had no radiographic OA. MRI bone surfaces were automatically segmented and aligned using active appearance models. We applied t-tests, logistic regression and linear discriminant analysis to compare 13 imaging features (including patella position, trochlear morphology, facet area and tilt) converted into 3D equivalents, and a measure of overall 3D shape. One hundred and fifteen knees with PFP (mean age 59.7, BMI 27.5 kg/m2, female 58.2%) and 438 without PFP (mean age 63.6, BMI 26.9 kg/m2, female 52.9%) were included. After correction for multiple testing, no statistically significant differences were found between groups for any of the 3D imaging features or their combinations. A statistically significant discrimination was noted for overall 3D shape between genders, confirming the validity of the 3D measures. Challenging current perceptions, no differences in patellofemoral morphology were found between older people with and without PFP using 3D quantitative imaging analysis. Further work is needed to see if these findings are replicated in a younger PFP population. © The Author 2017. Published by Oxford University Press on behalf of the British Society for Rheumatology.

Magnetic resonance imaging features of complex Chiari malformation variant of Chiari 1 malformation.

PubMed

Moore, Hannah E; Moore, Kevin R

2014-11-01

Complex Chiari malformation is a subgroup of Chiari 1 malformation with distinct imaging features. Children with complex Chiari malformation are reported to have a more severe clinical phenotype and sometimes require more extensive surgical treatment than those with uncomplicated Chiari 1 malformation. We describe reported MR imaging features of complex Chiari malformation and evaluate the utility of craniometric parameters and qualitative anatomical observations for distinguishing complex Chiari malformation from uncomplicated Chiari 1 malformation. We conducted a retrospective search of the institutional imaging database using the keywords "Chiari" and "Chiari 1" to identify children imaged during the 2006-2011 time period. Children with Chiari 2 malformation were excluded after imaging review. We used the first available diagnostic brain or cervical spine MR study for data measurement. Standard measurements and observations were made of obex level (mm), cerebellar tonsillar descent (mm), perpendicular distance to basion-C2 line (pB-C2, mm), craniocervical angle (degrees), clivus length, and presence or absence of syringohydromyelia, basilar invagination and congenital craniovertebral junction osseous anomalies. After imaging review, we accessed the institutional health care clinical database to determine whether each subject clinically met criteria for Chiari 1 malformation or complex Chiari malformation. Obex level and craniocervical angle measurements showed statistically significant differences between the populations with complex Chiari malformation and uncomplicated Chiari 1 malformation. Cerebellar tonsillar descent and perpendicular distance to basion-C2 line measurements trended toward but did not meet statistical significance. Odontoid retroflexion, craniovertebral junction osseous anomalies, and syringohydromyelia were all observed proportionally more often in children with complex Chiari malformation than in those with Chiari 1 malformation. Characteristic imaging features of complex Chiari malformation, especially obex level, permit its distinction from the more common uncomplicated Chiari 1 malformation.

Characteristics of Dry Chin-Tuck Swallowing Vibrations and Sounds

PubMed Central

Dudik, Joshua M; Jestrović, Iva; Luan, Bo; Coyle, James L.; Sejdić, Ervin

2015-01-01

Objective The effects of the chin-tuck maneuver, a technique commonly employed to compensate for dysphagia, on cervical auscultation are not fully understood. Characterizing a technique that is known to affect swallowing function is an important step on the way to developing a new instrumentation-based swallowing screening tool. Methods In this study, we recorded data from 55 adult participants who each completed five saliva swallows in a chin-tuck position. The resulting data was processed using previously designed filtering and segmentation algorithms. We then calculated 9 time, frequency, and time-frequency domain features for each independent signal. Results We found that multiple frequency and time domain features varied significantly between male and female subjects as well as between swallowing sounds and vibrations. However, our analysis showed that participant age did not play a significant role on the values of the extracted features. Finally, we found that various frequency features corresponding to swallowing vibrations did demonstrate statistically significant variation between the neutral and chin-tuck positions but sounds showed no changes between these two positions. Conclusion The chin-tuck maneuver affects many facets of swallowing vibrations and sounds and its effects can be monitored via cervical auscultation. Significance These results suggest that a subject’s swallowing technique does need to be accounted for when monitoring their performance with cervical auscultation based instrumentation. PMID:25974926

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

Bagher-Ebadian, H; Chetty, I; Liu, C

Purpose: To examine the impact of image smoothing and noise on the robustness of textural information extracted from CBCT images for prediction of radiotherapy response for patients with head/neck (H/N) cancers. Methods: CBCT image datasets for 14 patients with H/N cancer treated with radiation (70 Gy in 35 fractions) were investigated. A deformable registration algorithm was used to fuse planning CT’s to CBCT’s. Tumor volume was automatically segmented on each CBCT image dataset. Local control at 1-year was used to classify 8 patients as responders (R), and 6 as non-responders (NR). A smoothing filter [2D Adaptive Weiner (2DAW) with 3more » different windows (ψ=3, 5, and 7)], and two noise models (Poisson and Gaussian, SNR=25) were implemented, and independently applied to CBCT images. Twenty-two textural features, describing the spatial arrangement of voxel intensities calculated from gray-level co-occurrence matrices, were extracted for all tumor volumes. Results: Relative to CBCT images without smoothing, none of 22 textural features extracted showed any significant differences when smoothing was applied (using the 2DAW with filtering parameters of ψ=3 and 5), in the responder and non-responder groups. When smoothing, 2DAW with ψ=7 was applied, one textural feature, Information Measure of Correlation, was significantly different relative to no smoothing. Only 4 features (Energy, Entropy, Homogeneity, and Maximum-Probability) were found to be statistically different between the R and NR groups (Table 1). These features remained statistically significant discriminators for R and NR groups in presence of noise and smoothing. Conclusion: This preliminary work suggests that textural classifiers for response prediction, extracted from H&N CBCT images, are robust to low-power noise and low-pass filtering. While other types of filters will alter the spatial frequencies differently, these results are promising. The current study is subject to Type II errors. A much larger cohort of patients is needed to confirm these results. This work was supported in part by a grant from Varian Medical Systems (Palo Alto, CA)« less

Automated Cognitive Health Assessment From Smart Home-Based Behavior Data.

PubMed

Dawadi, Prafulla Nath; Cook, Diane Joyce; Schmitter-Edgecombe, Maureen

2016-07-01

Smart home technologies offer potential benefits for assisting clinicians by automating health monitoring and well-being assessment. In this paper, we examine the actual benefits of smart home-based analysis by monitoring daily behavior in the home and predicting clinical scores of the residents. To accomplish this goal, we propose a clinical assessment using activity behavior (CAAB) approach to model a smart home resident's daily behavior and predict the corresponding clinical scores. CAAB uses statistical features that describe characteristics of a resident's daily activity performance to train machine learning algorithms that predict the clinical scores. We evaluate the performance of CAAB utilizing smart home sensor data collected from 18 smart homes over two years. We obtain a statistically significant correlation ( r=0.72) between CAAB-predicted and clinician-provided cognitive scores and a statistically significant correlation ( r=0.45) between CAAB-predicted and clinician-provided mobility scores. These prediction results suggest that it is feasible to predict clinical scores using smart home sensor data and learning-based data analysis.

Particle-sampling statistics in laser anemometers Sample-and-hold systems and saturable systems

NASA Technical Reports Server (NTRS)

Edwards, R. V.; Jensen, A. S.

1983-01-01

The effect of the data-processing system on the particle statistics obtained with laser anemometry of flows containing suspended particles is examined. Attention is given to the sample and hold processor, a pseudo-analog device which retains the last measurement until a new measurement is made, followed by time-averaging of the data. The second system considered features a dead time, i.e., a saturable system with a significant reset time with storage in a data buffer. It is noted that the saturable system operates independent of the particle arrival rate. The probabilities of a particle arrival in a given time period are calculated for both processing systems. It is shown that the system outputs are dependent on the mean particle flow rate, the flow correlation time, and the flow statistics, indicating that the particle density affects both systems. The results are significant for instances of good correlation between the particle density and velocity, such as occurs near the edge of a jet.

Turbulence Measurements of Separate Flow Nozzles with Mixing Enhancement Features

NASA Technical Reports Server (NTRS)

Bridges, James; Wernet, Mark P.

2002-01-01

Comparison of turbulence data taken in three separate flow nozzles, two with mixing enhancement features on their core nozzle, shows how the mixing enhancement features modify turbulence to reduce jet noise. The three nozzles measured were the baseline axisymmetric nozzle 3BB, the alternating chevron nozzle, 3A12B, with 6-fold symmetry, and the flipper tab nozzle 3T24B also with 6-fold symmetry. The data presented show the differences in turbulence characteristics produced by the geometric differences in the nozzles, with emphasis on those characteristics of interest in jet noise. Among the significant findings: the enhanced mixing devices reduce turbulence in the jet mixing region while increasing it in the fan/core shear layer, the ratios of turbulence components are significantly altered by the mixing devices, and the integral lengthscales do not conform to any turbulence model yet proposed. These findings should provide guidance for modeling the statistical properties of turbulence to improve jet noise prediction.

Indications for axillary ultrasound use in breast cancer patients.

PubMed

Joh, Jennifer E; Han, Gang; Kiluk, John V; Laronga, Christine; Khakpour, Nazanin; Lee, M Catherine

2012-12-01

Axillary ultrasound has been adopted for preoperative planning in breast cancer. Our objective was to determine features predictive of abnormal AUS and/or positive axillary node needle biopsy (NBx). Single-institution database of breast cancer patients identified patients with preoperative AUS. Patient characteristics and outcomes were correlated with AUS and NBx. Significant features were identified using univariable and multivariable analysis and correlative statistics. Three hundred thirteen breast cancers were evaluated. Abnormal AUS was demonstrated in 250 cases (80%). Node needle biopsy was performed in 247 cases (79%). Sensitivity and specificity was 93% and 48% for AUS and 86% and 100% for NBx, respectively. Palpable axillary adenopathy was significant in logistic regression model (P < .05). There were positive correlations between tumor grade, clinical T and tumor-node-metastasis stage, invasive ductal carcinoma histology, and inflammatory breast carcinoma with AUS and NBx (P < .05). Clinicopathologic features (grade, histology, tumor size) might help guide judicious use of AUS. Copyright © 2012 Elsevier Inc. All rights reserved.

Exhaled isoprene for monitoring recovery from acute hypoxic stress.

PubMed

Harshman, Sean W; Geier, Brian A; Qualley, Anthony V; Drummond, Leslie A; Flory, Laura E; Fan, Maomian; Pitsch, Rhonda L; Grigsby, Claude C; Phillips, Jeffrey B; Martin, Jennifer A

2017-11-29

Hypoxia-like incidents in-flight have increased over the past decade causing severe safety concerns across the aviation community. As a result, the need to monitor flight crews in real-time for the onset of hypoxic conditions is paramount for continued aeronautical safety. Here, hypoxic events were simulated in the laboratory via a reduced oxygen-breathing device to determine the effect of recovery gas oxygen concentration (21% and 100%) on exhaled breath volatile organic compound composition. Data from samples collected both serially (throughout the exposure), prior to, and following exposures yielded 326 statistically significant features, 203 of which were unique. Of those, 72 features were tentatively identified while 51 were verified with authentic standards. A comparison of samples collected serially between recovery and hypoxia time points shows a statistically significant reduction in exhaled breath isoprene (2-methyl-1,3-butadiene, log 2 FC -0.399, p = 0.005, FDR = 0.034, q = 0.033), however no significant difference in isoprene abundance was observed when comparing recovery gases (21% or 100% O 2 , p = 0.152). Furthermore, examination of pre-/post-exposure 1 l bag breath samples illustrate an overall increase in exhaled isoprene abundance post-exposure (log 2 FC 0.393, p = 0.005, FDR = 0.094, q = 0.033) but again no significant difference between recovery gas (21% and 100%, p = 0.798) was observed. A statistically significant difference in trend was observed between isoprene abundance and recovery gases O 2 concentration when plotted against minimum oxygen saturation (p = 0.0419 100% O 2 , p = 0.7034 21% O 2 ). Collectively, these results suggest exhaled isoprene is dynamic in the laboratory ROBD setup and additional experimentation will be required to fully understand the dynamics of isoprene in response to acute hypoxic stress.

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

Liu, Richen; Guo, Hanqi; Yuan, Xiaoru

Most of the existing approaches to visualize vector field ensembles are to reveal the uncertainty of individual variables, for example, statistics, variability, etc. However, a user-defined derived feature like vortex or air mass is also quite significant, since they make more sense to domain scientists. In this paper, we present a new framework to extract user-defined derived features from different simulation runs. Specially, we use a detail-to-overview searching scheme to help extract vortex with a user-defined shape. We further compute the geometry information including the size, the geo-spatial location of the extracted vortexes. We also design some linked views tomore » compare them between different runs. At last, the temporal information such as the occurrence time of the feature is further estimated and compared. Results show that our method is capable of extracting the features across different runs and comparing them spatially and temporally.« less

Uterine Fibroid Embolization for Symptomatic Fibroids: Study at a Teaching Hospital in Kenya

PubMed Central

Mutai, John Kiprop; Vinayak, Sudhir; Stones, William; Hacking, Nigel; Mariara, Charles

2015-01-01

Objective: Characterization of magnetic (MRI) features in women undergoing uterine fibroid embolization (UFE) and identification of clinical correlates in an African population. Materials and Methods: Patients with symptomatic fibroids who are selected to undergo UFE at the hospital formed the study population. The baseline MRI features, baseline symptom score, short-term imaging outcome, and mid-term symptom scores were analyzed for interval changes. Assessment of potential associations between short-term imaging features and mid-term symptom scores was also done. Results: UFE resulted in statistically significant reduction (P < 0.001) of dominant fibroid, uterine volumes, and reduction of symptom severity scores, which were 43.7%, 40.1%, and 37.8%, respectively. Also, 59% of respondents had more than 10 fibroids. The predominant location of the dominant fibroid was intramural. No statistically significant association was found between clinical and radiological outcome. Conclusion: The response of uterine fibroids to embolization in the African population is not different from the findings reported in other studies from the west. The presence of multiple and large fibroids in this study is consistent with the case mix described in other studies of African-American populations. Patient counseling should emphasize the independence of volume reduction and symptom improvement. Though volume changes are of relevance for the radiologist in understanding the evolution of the condition and identifying potential technical treatment failures, it should not be the main basis of evaluation of treatment success. PMID:25883858

Statistical Methods for Detecting Differentially Abundant Features in Clinical Metagenomic Samples

PubMed Central

White, James Robert; Nagarajan, Niranjan; Pop, Mihai

2009-01-01

Numerous studies are currently underway to characterize the microbial communities inhabiting our world. These studies aim to dramatically expand our understanding of the microbial biosphere and, more importantly, hope to reveal the secrets of the complex symbiotic relationship between us and our commensal bacterial microflora. An important prerequisite for such discoveries are computational tools that are able to rapidly and accurately compare large datasets generated from complex bacterial communities to identify features that distinguish them. We present a statistical method for comparing clinical metagenomic samples from two treatment populations on the basis of count data (e.g. as obtained through sequencing) to detect differentially abundant features. Our method, Metastats, employs the false discovery rate to improve specificity in high-complexity environments, and separately handles sparsely-sampled features using Fisher's exact test. Under a variety of simulations, we show that Metastats performs well compared to previously used methods, and significantly outperforms other methods for features with sparse counts. We demonstrate the utility of our method on several datasets including a 16S rRNA survey of obese and lean human gut microbiomes, COG functional profiles of infant and mature gut microbiomes, and bacterial and viral metabolic subsystem data inferred from random sequencing of 85 metagenomes. The application of our method to the obesity dataset reveals differences between obese and lean subjects not reported in the original study. For the COG and subsystem datasets, we provide the first statistically rigorous assessment of the differences between these populations. The methods described in this paper are the first to address clinical metagenomic datasets comprising samples from multiple subjects. Our methods are robust across datasets of varied complexity and sampling level. While designed for metagenomic applications, our software can also be applied to digital gene expression studies (e.g. SAGE). A web server implementation of our methods and freely available source code can be found at http://metastats.cbcb.umd.edu/. PMID:19360128

Mammographic texture synthesis using genetic programming and clustered lumpy background

NASA Astrophysics Data System (ADS)

Castella, Cyril; Kinkel, Karen; Descombes, François; Eckstein, Miguel P.; Sottas, Pierre-Edouard; Verdun, Francis R.; Bochud, François O.

2006-03-01

In this work we investigated the digital synthesis of images which mimic real textures observed in mammograms. Such images could be produced in an unlimited number with tunable statistical properties in order to study human performance and model observer performance in perception experiments. We used the previously developed clustered lumpy background (CLB) technique and optimized its parameters with a genetic algorithm (GA). In order to maximize the realism of the textures, we combined the GA objective approach with psychophysical experiments involving the judgments of radiologists. Thirty-six statistical features were computed and averaged, over 1000 real mammograms regions of interest. The same features were measured for the synthetic textures, and the Mahalanobis distance was used to quantify the similarity of the features between the real and synthetic textures. The similarity, as measured by the Mahalanobis distance, was used as GA fitness function for evolving the free CLB parameters. In the psychophysical approach, experienced radiologists were asked to qualify the realism of synthetic images by considering typical structures that are expected to be found on real mammograms: glandular and fatty areas, and fiber crossings. Results show that CLB images found via optimization with GA are significantly closer to real mammograms than previously published images. Moreover, the psychophysical experiments confirm that all the above mentioned structures are reproduced well on the generated images. This means that we can generate an arbitrary large database of textures mimicking mammograms with traceable statistical properties.

Standardization of infrared breast thermogram acquisition protocols and abnormality analysis of breast thermograms

NASA Astrophysics Data System (ADS)

Bhowmik, Mrinal Kanti; Gogoi, Usha Rani; Das, Kakali; Ghosh, Anjan Kumar; Bhattacharjee, Debotosh; Majumdar, Gautam

2016-05-01

The non-invasive, painless, radiation-free and cost-effective infrared breast thermography (IBT) makes a significant contribution to improving the survival rate of breast cancer patients by early detecting the disease. This paper presents a set of standard breast thermogram acquisition protocols to improve the potentiality and accuracy of infrared breast thermograms in early breast cancer detection. By maintaining all these protocols, an infrared breast thermogram acquisition setup has been established at the Regional Cancer Centre (RCC) of Government Medical College (AGMC), Tripura, India. The acquisition of breast thermogram is followed by the breast thermogram interpretation, for identifying the presence of any abnormality. However, due to the presence of complex vascular patterns, accurate interpretation of breast thermogram is a very challenging task. The bilateral symmetry of the thermal patterns in each breast thermogram is quantitatively computed by statistical feature analysis. A series of statistical features are extracted from a set of 20 thermograms of both healthy and unhealthy subjects. Finally, the extracted features are analyzed for breast abnormality detection. The key contributions made by this paper can be highlighted as -- a) the designing of a standard protocol suite for accurate acquisition of breast thermograms, b) creation of a new breast thermogram dataset by maintaining the protocol suite, and c) statistical analysis of the thermograms for abnormality detection. By doing so, this proposed work can minimize the rate of false findings in breast thermograms and thus, it will increase the utilization potentiality of breast thermograms in early breast cancer detection.

SEGMENTING CT PROSTATE IMAGES USING POPULATION AND PATIENT-SPECIFIC STATISTICS FOR RADIOTHERAPY.

PubMed

Feng, Qianjin; Foskey, Mark; Tang, Songyuan; Chen, Wufan; Shen, Dinggang

2009-08-07

This paper presents a new deformable model using both population and patient-specific statistics to segment the prostate from CT images. There are two novelties in the proposed method. First, a modified scale invariant feature transform (SIFT) local descriptor, which is more distinctive than general intensity and gradient features, is used to characterize the image features. Second, an online training approach is used to build the shape statistics for accurately capturing intra-patient variation, which is more important than inter-patient variation for prostate segmentation in clinical radiotherapy. Experimental results show that the proposed method is robust and accurate, suitable for clinical application.

SEGMENTING CT PROSTATE IMAGES USING POPULATION AND PATIENT-SPECIFIC STATISTICS FOR RADIOTHERAPY

PubMed Central

Feng, Qianjin; Foskey, Mark; Tang, Songyuan; Chen, Wufan; Shen, Dinggang

2010-01-01

This paper presents a new deformable model using both population and patient-specific statistics to segment the prostate from CT images. There are two novelties in the proposed method. First, a modified scale invariant feature transform (SIFT) local descriptor, which is more distinctive than general intensity and gradient features, is used to characterize the image features. Second, an online training approach is used to build the shape statistics for accurately capturing intra-patient variation, which is more important than inter-patient variation for prostate segmentation in clinical radiotherapy. Experimental results show that the proposed method is robust and accurate, suitable for clinical application. PMID:21197416

Effects of preprocessing Landsat MSS data on derived features

NASA Technical Reports Server (NTRS)

Parris, T. M.; Cicone, R. C.

1983-01-01

Important to the use of multitemporal Landsat MSS data for earth resources monitoring, such as agricultural inventories, is the ability to minimize the effects of varying atmospheric and satellite viewing conditions, while extracting physically meaningful features from the data. In general, the approaches to the preprocessing problem have been derived from either physical or statistical models. This paper compares three proposed algorithms; XSTAR haze correction, Color Normalization, and Multiple Acquisition Mean Level Adjustment. These techniques represent physical, statistical, and hybrid physical-statistical models, respectively. The comparisons are made in the context of three feature extraction techniques; the Tasseled Cap, the Cate Color Cube. and Normalized Difference.

Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis.

PubMed

Luo, Xin; Zang, Xiao; Yang, Lin; Huang, Junzhou; Liang, Faming; Rodriguez-Canales, Jaime; Wistuba, Ignacio I; Gazdar, Adi; Xie, Yang; Xiao, Guanghua

2017-03-01

Pathological examination of histopathological slides is a routine clinical procedure for lung cancer diagnosis and prognosis. Although the classification of lung cancer has been updated to become more specific, only a small subset of the total morphological features are taken into consideration. The vast majority of the detailed morphological features of tumor tissues, particularly tumor cells' surrounding microenvironment, are not fully analyzed. The heterogeneity of tumor cells and close interactions between tumor cells and their microenvironments are closely related to tumor development and progression. The goal of this study is to develop morphological feature-based prediction models for the prognosis of patients with lung cancer. We developed objective and quantitative computational approaches to analyze the morphological features of pathological images for patients with NSCLC. Tissue pathological images were analyzed for 523 patients with adenocarcinoma (ADC) and 511 patients with squamous cell carcinoma (SCC) from The Cancer Genome Atlas lung cancer cohorts. The features extracted from the pathological images were used to develop statistical models that predict patients' survival outcomes in ADC and SCC, respectively. We extracted 943 morphological features from pathological images of hematoxylin and eosin-stained tissue and identified morphological features that are significantly associated with prognosis in ADC and SCC, respectively. Statistical models based on these extracted features stratified NSCLC patients into high-risk and low-risk groups. The models were developed from training sets and validated in independent testing sets: a predicted high-risk group versus a predicted low-risk group (for patients with ADC: hazard ratio = 2.34, 95% confidence interval: 1.12-4.91, p = 0.024; for patients with SCC: hazard ratio = 2.22, 95% confidence interval: 1.15-4.27, p = 0.017) after adjustment for age, sex, smoking status, and pathologic tumor stage. The results suggest that the quantitative morphological features of tumor pathological images predict prognosis in patients with lung cancer. Copyright © 2016 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE.

PubMed

Quaglio, Pietro; Yegenoglu, Alper; Torre, Emiliano; Endres, Dominik M; Grün, Sonja

2017-01-01

Repeated, precise sequences of spikes are largely considered a signature of activation of cell assemblies. These repeated sequences are commonly known under the name of spatio-temporal patterns (STPs). STPs are hypothesized to play a role in the communication of information in the computational process operated by the cerebral cortex. A variety of statistical methods for the detection of STPs have been developed and applied to electrophysiological recordings, but such methods scale poorly with the current size of available parallel spike train recordings (more than 100 neurons). In this work, we introduce a novel method capable of overcoming the computational and statistical limits of existing analysis techniques in detecting repeating STPs within massively parallel spike trains (MPST). We employ advanced data mining techniques to efficiently extract repeating sequences of spikes from the data. Then, we introduce and compare two alternative approaches to distinguish statistically significant patterns from chance sequences. The first approach uses a measure known as conceptual stability, of which we investigate a computationally cheap approximation for applications to such large data sets. The second approach is based on the evaluation of pattern statistical significance. In particular, we provide an extension to STPs of a method we recently introduced for the evaluation of statistical significance of synchronous spike patterns. The performance of the two approaches is evaluated in terms of computational load and statistical power on a variety of artificial data sets that replicate specific features of experimental data. Both methods provide an effective and robust procedure for detection of STPs in MPST data. The method based on significance evaluation shows the best overall performance, although at a higher computational cost. We name the novel procedure the spatio-temporal Spike PAttern Detection and Evaluation (SPADE) analysis.

Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE

PubMed Central

Quaglio, Pietro; Yegenoglu, Alper; Torre, Emiliano; Endres, Dominik M.; Grün, Sonja

2017-01-01

Repeated, precise sequences of spikes are largely considered a signature of activation of cell assemblies. These repeated sequences are commonly known under the name of spatio-temporal patterns (STPs). STPs are hypothesized to play a role in the communication of information in the computational process operated by the cerebral cortex. A variety of statistical methods for the detection of STPs have been developed and applied to electrophysiological recordings, but such methods scale poorly with the current size of available parallel spike train recordings (more than 100 neurons). In this work, we introduce a novel method capable of overcoming the computational and statistical limits of existing analysis techniques in detecting repeating STPs within massively parallel spike trains (MPST). We employ advanced data mining techniques to efficiently extract repeating sequences of spikes from the data. Then, we introduce and compare two alternative approaches to distinguish statistically significant patterns from chance sequences. The first approach uses a measure known as conceptual stability, of which we investigate a computationally cheap approximation for applications to such large data sets. The second approach is based on the evaluation of pattern statistical significance. In particular, we provide an extension to STPs of a method we recently introduced for the evaluation of statistical significance of synchronous spike patterns. The performance of the two approaches is evaluated in terms of computational load and statistical power on a variety of artificial data sets that replicate specific features of experimental data. Both methods provide an effective and robust procedure for detection of STPs in MPST data. The method based on significance evaluation shows the best overall performance, although at a higher computational cost. We name the novel procedure the spatio-temporal Spike PAttern Detection and Evaluation (SPADE) analysis. PMID:28596729

DEIVA: a web application for interactive visual analysis of differential gene expression profiles.

PubMed

Harshbarger, Jayson; Kratz, Anton; Carninci, Piero

2017-01-07

Differential gene expression (DGE) analysis is a technique to identify statistically significant differences in RNA abundance for genes or arbitrary features between different biological states. The result of a DGE test is typically further analyzed using statistical software, spreadsheets or custom ad hoc algorithms. We identified a need for a web-based system to share DGE statistical test results, and locate and identify genes in DGE statistical test results with a very low barrier of entry. We have developed DEIVA, a free and open source, browser-based single page application (SPA) with a strong emphasis on being user friendly that enables locating and identifying single or multiple genes in an immediate, interactive, and intuitive manner. By design, DEIVA scales with very large numbers of users and datasets. Compared to existing software, DEIVA offers a unique combination of design decisions that enable inspection and analysis of DGE statistical test results with an emphasis on ease of use.

Application of LANDSAT system for improving methodology for inventory and classification of wetlands

NASA Technical Reports Server (NTRS)

Gilmer, D. S. (Principal Investigator)

1976-01-01

The author has identified the following significant results. A newly developed software system for generating statistics on surface water features was tested using LANDSAT data acquired previous to 1975. This software test provided a satisfactory evaluation of the system and also allowed expansion of data base on prairie water features. The software system recognizes water on the basis of a classification algorithm. This classification is accomplished by level thresholding a single near infrared data channel. After each pixel is classified as water or nonwater, the software system then recognizes ponds or lakes as sets of contiguous pixels or as single isolated pixels in the case of very small ponds. Pixels are considered to be contiguous if they are adjacent between successive scan lines. After delineating each water feature, the software system then assigns the feature a position based upon a geographic grid system and calculates the feature's planimetric area, its perimeter, and a parameter known as the shape factor.

Fuzzy membership functions for analysis of high-resolution CT images of diffuse pulmonary diseases.

PubMed

Almeida, Eliana; Rangayyan, Rangaraj M; Azevedo-Marques, Paulo M

2015-08-01

We propose the use of fuzzy membership functions to analyze images of diffuse pulmonary diseases (DPDs) based on fractal and texture features. The features were extracted from preprocessed regions of interest (ROIs) selected from high-resolution computed tomography images. The ROIs represent five different patterns of DPDs and normal lung tissue. A Gaussian mixture model (GMM) was constructed for each feature, with six Gaussians modeling the six patterns. Feature selection was performed and the GMMs of the five significant features were used. From the GMMs, fuzzy membership functions were obtained by a probability-possibility transformation and further statistical analysis was performed. An average classification accuracy of 63.5% was obtained for the six classes. For four of the six classes, the classification accuracy was superior to 65%, and the best classification accuracy was 75.5% for one class. The use of fuzzy membership functions to assist in pattern classification is an alternative to deterministic approaches to explore strategies for medical diagnosis.

Prevalence of Body Dysmorphic Disorder and its Association With Body Features in Female Medical Students.

PubMed

Shaffi Ahamed, Shaik; Enani, Jawaher; Alfaraidi, Lama; Sannari, Lujain; Algain, Rihaf; Alsawah, Zainah; Al Hazmi, Ali

2016-06-01

Body dysmorphic disorder (BDD) is a distressing psychiatric disorder. So far there have not been any studies on BDD in Saudi Arabia. The aim of this study was to determine the prevalence of body dysmorphic disorder in female medical students and to investigate whether there is an association between BDD and body features of concern, social anxiety and symptoms of BDD. A cross sectional study was carried out on female medical students of the college of medicine, King Saud University, Riyadh, Saudi Arabia during January to April, 2015. Data were collected using the body image disturbance questionnaire, Body dysmorphic disorder symptomatology and social interaction anxiety scale. Descriptive statistics, bivariate and multivariate analysis were used to analyze the results. Out of 365 students who filled out the questionnaire, 4.4% (95% confidence intervals (CI): 2.54% to 7.04%) were positive for BDD with skin (75%) and fat (68.8%) as the most frequent body features of concern. Ten features (skin, fat, chest, hips, buttocks, arms, legs, lips, fingers, and shoulders) out of twenty-six were significantly associated with BDD. Arms and chest were independently associated with BDD. The odds of presence of body concern related to "arms" was 4.3 (95% C.I: 1.5, 12.1) times more in BDD subjects than non-BDD subjects, while concern about "chest" was 3.8 (1.3, 10.9) times more when compared to non-BDD subjects. No statistically significant association was observed between BDD and social anxiety (P = 0.13). This was the first study conducted in Kingdom of Saudi Arabia (KSA) on female medical students, which quantified the prevalence of BDD and identified the body features associated with it. Body dysmorphic disorder is prevalent in female medical students but it is relatively rare and an unnoticed disorder.

Prevalence of Body Dysmorphic Disorder and its Association With Body Features in Female Medical Students

PubMed Central

Shaffi Ahamed, Shaik; Enani, Jawaher; Alfaraidi, Lama; Sannari, Lujain; Algain, Rihaf; Alsawah, Zainah; Al Hazmi, Ali

2016-01-01

Background Body dysmorphic disorder (BDD) is a distressing psychiatric disorder. So far there have not been any studies on BDD in Saudi Arabia. Objectives The aim of this study was to determine the prevalence of body dysmorphic disorder in female medical students and to investigate whether there is an association between BDD and body features of concern, social anxiety and symptoms of BDD. Materials and Methods A cross sectional study was carried out on female medical students of the college of medicine, King Saud University, Riyadh, Saudi Arabia during January to April, 2015. Data were collected using the body image disturbance questionnaire, Body dysmorphic disorder symptomatology and social interaction anxiety scale. Descriptive statistics, bivariate and multivariate analysis were used to analyze the results. Results Out of 365 students who filled out the questionnaire, 4.4% (95% confidence intervals (CI): 2.54% to 7.04%) were positive for BDD with skin (75%) and fat (68.8%) as the most frequent body features of concern. Ten features (skin, fat, chest, hips, buttocks, arms, legs, lips, fingers, and shoulders) out of twenty-six were significantly associated with BDD. Arms and chest were independently associated with BDD. The odds of presence of body concern related to “arms” was 4.3 (95% C.I: 1.5, 12.1) times more in BDD subjects than non-BDD subjects, while concern about “chest” was 3.8 (1.3, 10.9) times more when compared to non-BDD subjects. No statistically significant association was observed between BDD and social anxiety (P = 0.13). Conclusions This was the first study conducted in Kingdom of Saudi Arabia (KSA) on female medical students, which quantified the prevalence of BDD and identified the body features associated with it. Body dysmorphic disorder is prevalent in female medical students but it is relatively rare and an unnoticed disorder. PMID:27803720

A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability.

PubMed

Awais, Muhammad; Badruddin, Nasreen; Drieberg, Micheal

2017-08-31

Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-based study. A monotonous driving environment is used to induce drowsiness in the participants. Various time and frequency domain feature were extracted from EEG including time domain statistical descriptors, complexity measures and power spectral measures. Features extracted from the ECG signal included heart rate (HR) and heart rate variability (HRV), including low frequency (LF), high frequency (HF) and LF/HF ratio. Furthermore, subjective sleepiness scale is also assessed to study its relationship with drowsiness. We used paired t -tests to select only statistically significant features ( p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features of both modalities (EEG and ECG) are then combined to investigate the improvement in performance using support vector machine (SVM) classifier. The other main contribution of this paper is the study on channel reduction and its impact to the performance of detection. The proposed method demonstrated that combining EEG and ECG has improved the system's performance in discriminating between alert and drowsy states, instead of using them alone. Our channel reduction analysis revealed that an acceptable level of accuracy (80%) could be achieved by combining just two electrodes (one EEG and one ECG), indicating the feasibility of a system with improved wearability compared with existing systems involving many electrodes. Overall, our results demonstrate that the proposed method can be a viable solution for a practical driver drowsiness system that is both accurate and comfortable to wear.

A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability

PubMed Central

Badruddin, Nasreen

2017-01-01

Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-based study. A monotonous driving environment is used to induce drowsiness in the participants. Various time and frequency domain feature were extracted from EEG including time domain statistical descriptors, complexity measures and power spectral measures. Features extracted from the ECG signal included heart rate (HR) and heart rate variability (HRV), including low frequency (LF), high frequency (HF) and LF/HF ratio. Furthermore, subjective sleepiness scale is also assessed to study its relationship with drowsiness. We used paired t-tests to select only statistically significant features (p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features of both modalities (EEG and ECG) are then combined to investigate the improvement in performance using support vector machine (SVM) classifier. The other main contribution of this paper is the study on channel reduction and its impact to the performance of detection. The proposed method demonstrated that combining EEG and ECG has improved the system’s performance in discriminating between alert and drowsy states, instead of using them alone. Our channel reduction analysis revealed that an acceptable level of accuracy (80%) could be achieved by combining just two electrodes (one EEG and one ECG), indicating the feasibility of a system with improved wearability compared with existing systems involving many electrodes. Overall, our results demonstrate that the proposed method can be a viable solution for a practical driver drowsiness system that is both accurate and comfortable to wear. PMID:28858220

A statistical parts-based appearance model of inter-subject variability.

PubMed

Toews, Matthew; Collins, D Louis; Arbel, Tal

2006-01-01

In this article, we present a general statistical parts-based model for representing the appearance of an image set, applied to the problem of inter-subject MR brain image matching. In contrast with global image representations such as active appearance models, the parts-based model consists of a collection of localized image parts whose appearance, geometry and occurrence frequency are quantified statistically. The parts-based approach explicitly addresses the case where one-to-one correspondence does not exist between subjects due to anatomical differences, as parts are not expected to occur in all subjects. The model can be learned automatically, discovering structures that appear with statistical regularity in a large set of subject images, and can be robustly fit to new images, all in the presence of significant inter-subject variability. As parts are derived from generic scale-invariant features, the framework can be applied in a wide variety of image contexts, in order to study the commonality of anatomical parts or to group subjects according to the parts they share. Experimentation shows that a parts-based model can be learned from a large set of MR brain images, and used to determine parts that are common within the group of subjects. Preliminary results indicate that the model can be used to automatically identify distinctive features for inter-subject image registration despite large changes in appearance.

Multivariate Analysis, Mass Balance Techniques, and Statistical Tests as Tools in Igneous Petrology: Application to the Sierra de las Cruces Volcanic Range (Mexican Volcanic Belt)

PubMed Central

Velasco-Tapia, Fernando

2014-01-01

Magmatic processes have usually been identified and evaluated using qualitative or semiquantitative geochemical or isotopic tools based on a restricted number of variables. However, a more complete and quantitative view could be reached applying multivariate analysis, mass balance techniques, and statistical tests. As an example, in this work a statistical and quantitative scheme is applied to analyze the geochemical features for the Sierra de las Cruces (SC) volcanic range (Mexican Volcanic Belt). In this locality, the volcanic activity (3.7 to 0.5 Ma) was dominantly dacitic, but the presence of spheroidal andesitic enclaves and/or diverse disequilibrium features in majority of lavas confirms the operation of magma mixing/mingling. New discriminant-function-based multidimensional diagrams were used to discriminate tectonic setting. Statistical tests of discordancy and significance were applied to evaluate the influence of the subducting Cocos plate, which seems to be rather negligible for the SC magmas in relation to several major and trace elements. A cluster analysis following Ward's linkage rule was carried out to classify the SC volcanic rocks geochemical groups. Finally, two mass-balance schemes were applied for the quantitative evaluation of the proportion of the end-member components (dacitic and andesitic magmas) in the comingled lavas (binary mixtures). PMID:24737994

a Statistical Texture Feature for Building Collapse Information Extraction of SAR Image

NASA Astrophysics Data System (ADS)

Li, L.; Yang, H.; Chen, Q.; Liu, X.

2018-04-01

Synthetic Aperture Radar (SAR) has become one of the most important ways to extract post-disaster collapsed building information, due to its extreme versatility and almost all-weather, day-and-night working capability, etc. In view of the fact that the inherent statistical distribution of speckle in SAR images is not used to extract collapsed building information, this paper proposed a novel texture feature of statistical models of SAR images to extract the collapsed buildings. In the proposed feature, the texture parameter of G0 distribution from SAR images is used to reflect the uniformity of the target to extract the collapsed building. This feature not only considers the statistical distribution of SAR images, providing more accurate description of the object texture, but also is applied to extract collapsed building information of single-, dual- or full-polarization SAR data. The RADARSAT-2 data of Yushu earthquake which acquired on April 21, 2010 is used to present and analyze the performance of the proposed method. In addition, the applicability of this feature to SAR data with different polarizations is also analysed, which provides decision support for the data selection of collapsed building information extraction.

Optimizing methods for linking cinematic features to fMRI data.

PubMed

Kauttonen, Janne; Hlushchuk, Yevhen; Tikka, Pia

2015-04-15

One of the challenges of naturalistic neurosciences using movie-viewing experiments is how to interpret observed brain activations in relation to the multiplicity of time-locked stimulus features. As previous studies have shown less inter-subject synchronization across viewers of random video footage than story-driven films, new methods need to be developed for analysis of less story-driven contents. To optimize the linkage between our fMRI data collected during viewing of a deliberately non-narrative silent film 'At Land' by Maya Deren (1944) and its annotated content, we combined the method of elastic-net regularization with the model-driven linear regression and the well-established data-driven independent component analysis (ICA) and inter-subject correlation (ISC) methods. In the linear regression analysis, both IC and region-of-interest (ROI) time-series were fitted with time-series of a total of 36 binary-valued and one real-valued tactile annotation of film features. The elastic-net regularization and cross-validation were applied in the ordinary least-squares linear regression in order to avoid over-fitting due to the multicollinearity of regressors, the results were compared against both the partial least-squares (PLS) regression and the un-regularized full-model regression. Non-parametric permutation testing scheme was applied to evaluate the statistical significance of regression. We found statistically significant correlation between the annotation model and 9 ICs out of 40 ICs. Regression analysis was also repeated for a large set of cubic ROIs covering the grey matter. Both IC- and ROI-based regression analyses revealed activations in parietal and occipital regions, with additional smaller clusters in the frontal lobe. Furthermore, we found elastic-net based regression more sensitive than PLS and un-regularized regression since it detected a larger number of significant ICs and ROIs. Along with the ISC ranking methods, our regression analysis proved a feasible method for ordering the ICs based on their functional relevance to the annotated cinematic features. The novelty of our method is - in comparison to the hypothesis-driven manual pre-selection and observation of some individual regressors biased by choice - in applying data-driven approach to all content features simultaneously. We found especially the combination of regularized regression and ICA useful when analyzing fMRI data obtained using non-narrative movie stimulus with a large set of complex and correlated features. Copyright © 2015. Published by Elsevier Inc.

Rapid extraction of image texture by co-occurrence using a hybrid data structure

NASA Astrophysics Data System (ADS)

Clausi, David A.; Zhao, Yongping

2002-07-01

Calculation of co-occurrence probabilities is a popular method for determining texture features within remotely sensed digital imagery. Typically, the co-occurrence features are calculated by using a grey level co-occurrence matrix (GLCM) to store the co-occurring probabilities. Statistics are applied to the probabilities in the GLCM to generate the texture features. This method is computationally intensive since the matrix is usually sparse leading to many unnecessary calculations involving zero probabilities when applying the statistics. An improvement on the GLCM method is to utilize a grey level co-occurrence linked list (GLCLL) to store only the non-zero co-occurring probabilities. The GLCLL suffers since, to achieve preferred computational speeds, the list should be sorted. An improvement on the GLCLL is to utilize a grey level co-occurrence hybrid structure (GLCHS) based on an integrated hash table and linked list approach. Texture features obtained using this technique are identical to those obtained using the GLCM and GLCLL. The GLCHS method is implemented using the C language in a Unix environment. Based on a Brodatz test image, the GLCHS method is demonstrated to be a superior technique when compared across various window sizes and grey level quantizations. The GLCHS method required, on average, 33.4% ( σ=3.08%) of the computational time required by the GLCLL. Significant computational gains are made using the GLCHS method.

Type 1 diabetes mellitus effects on dental enamel formation revealed by microscopy and microanalysis.

PubMed

Silva, Bruna Larissa Lago; Medeiros, Danila Lima; Soares, Ana Prates; Line, Sérgio Roberto Peres; Pinto, Maria das Graças Farias; Soares, Telma de Jesus; do Espírito Santo, Alexandre Ribeiro

2018-03-01

Type 1 diabetes mellitus (T1DM) largely affects children, occurring therefore at the same period of deciduous and permanent teeth development. The aim of this work was to investigate birefringence and morphology of the secretory stage enamel organic extracellular matrix (EOECM), and structural and mechanical features of mature enamel from T1DM rats. Adult Wistar rats were maintained alive for a period of 56 days after the induction of experimental T1DM with a single dose of streptozotocin (60 mg/kg). After proper euthanasia of the animals, fixed upper incisors were accurately processed, and secretory stage EOECM and mature enamel were analyzed by transmitted polarizing and bright field light microscopies (TPLM and BFLM), energy-dispersive x-ray (EDX) analysis, scanning electron microscopy (SEM), and microhardness testing. Bright field light microscopies and transmitted polarizing light microscopies showed slight morphological changes in the secretory stage EOECM from diabetic rats, which also did not exhibit statistically significant alterations in birefringence brightness when compared to control animals (P > .05). EDX analysis showed that T1DM induced statistically significant little increases in the amount of calcium and phosphorus in outer mature enamel (P < .01) with preservation of calcium/phosphorus ratio in that structure (P > .05). T1DM also caused important ultrastructural alterations in mature enamel as revealed by SEM and induced a statistically significant reduction of about 13.67% in its microhardness at 80 μm from dentin-enamel junction (P < .01). This study shows that T1DM may disturb enamel development, leading to alterations in mature enamel ultrastructure and in its mechanical features. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

A comparative analysis of conventional cytopreparatory and liquid based cytological techniques (Sure Path) in evaluation of serous effusion fluids.

PubMed

Dadhich, Hrishikesh; Toi, Pampa Ch; Siddaraju, Neelaiah; Sevvanthi, Kalidas

2016-11-01

Clinically, detection of malignant cells in serous body fluids is critical, as their presence implies the upstaging of the disease. Cytology of body cavity fluids serves as an important tool when other diagnostic tests cannot be performed. In most laboratories, currently, the effusion fluid samples are analysed chiefly by the conventional cytopreparatory (CCP) technique. Although, there are several studies comparing the liquid-based cytology (LBC), with CCP technique in the field of cervicovaginal cytology; the literature on such comparison with respect to serous body fluid examination is sparse. One hundred samples of serous body fluids were processed by both CCP and LBC techniques. Slides prepared by these techniques were studied using six parameters. A comparative analysis of the advantages and disadvantages of the techniques in detection of malignant cells was carried out with appropriate statistical tests. The samples comprised 52 pleural, 44 peritoneal and four pericardial fluids. No statistically significant difference was noted with respect to cellularity (P values = 0.22), cell distribution (P values = 0.39) and diagnosis of malignancy (P values = 0.20). As for the remaining parameters, LBC provided statistically significant clearer smear background (P values < 0.0001) and shorter screening time (P values < 0.0001), while CPP technique provided a significantly better staining quality (P values 0.01) and sharper cytomorphologic features (P values 0.05). Although, a reduced screening time and clearer smear background are the two major advantages of LBC; the CCP technique provides the better staining quality with sharper cytomorphologic features which is more critical from the cytologic interpretation point of view. Diagn. Cytopathol. 2016;44:874-879. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Novel features and enhancements in BioBin, a tool for the biologically inspired binning and association analysis of rare variants

PubMed Central

Byrska-Bishop, Marta; Wallace, John; Frase, Alexander T; Ritchie, Marylyn D

2018-01-01

Abstract Motivation BioBin is an automated bioinformatics tool for the multi-level biological binning of sequence variants. Herein, we present a significant update to BioBin which expands the software to facilitate a comprehensive rare variant analysis and incorporates novel features and analysis enhancements. Results In BioBin 2.3, we extend our software tool by implementing statistical association testing, updating the binning algorithm, as well as incorporating novel analysis features providing for a robust, highly customizable, and unified rare variant analysis tool. Availability and implementation The BioBin software package is open source and freely available to users at http://www.ritchielab.com/software/biobin-download Contact mdritchie@geisinger.edu Supplementary information Supplementary data are available at Bioinformatics online. PMID:28968757

Phrase-Final Words in Greek Storytelling Speech: A Study on the Effect of a Culturally-Specific Prosodic Feature on Short-Term Memory.

PubMed

Loutrari, Ariadne; Tselekidou, Freideriki; Proios, Hariklia

2018-02-27

Prosodic patterns of speech appear to make a critical contribution to memory-related processing. We considered the case of a previously unexplored prosodic feature of Greek storytelling and its effect on free recall in thirty typically developing children between the ages of 10 and 12 years, using short ecologically valid auditory stimuli. The combination of a falling pitch contour and, more notably, extensive final-syllable vowel lengthening, which gives rise to the prosodic feature in question, led to statistically significantly higher performance in comparison to neutral phrase-final prosody. Number of syllables in target words did not reveal substantial difference in performance. The current study presents a previously undocumented culturally-specific prosodic pattern and its effect on short-term memory.

Associative memory model for searching an image database by image snippet

NASA Astrophysics Data System (ADS)

Khan, Javed I.; Yun, David Y.

1994-09-01

This paper presents an associative memory called an multidimensional holographic associative computing (MHAC), which can be potentially used to perform feature based image database query using image snippet. MHAC has the unique capability to selectively focus on specific segments of a query frame during associative retrieval. As a result, this model can perform search on the basis of featural significance described by a subset of the snippet pixels. This capability is critical for visual query in image database because quite often the cognitive index features in the snippet are statistically weak. Unlike, the conventional artificial associative memories, MHAC uses a two level representation and incorporates additional meta-knowledge about the reliability status of segments of information it receives and forwards. In this paper we present the analysis of focus characteristics of MHAC.

Histogram of gradient and binarized statistical image features of wavelet subband-based palmprint features extraction

NASA Astrophysics Data System (ADS)

Attallah, Bilal; Serir, Amina; Chahir, Youssef; Boudjelal, Abdelwahhab

2017-11-01

Palmprint recognition systems are dependent on feature extraction. A method of feature extraction using higher discrimination information was developed to characterize palmprint images. In this method, two individual feature extraction techniques are applied to a discrete wavelet transform of a palmprint image, and their outputs are fused. The two techniques used in the fusion are the histogram of gradient and the binarized statistical image features. They are then evaluated using an extreme learning machine classifier before selecting a feature based on principal component analysis. Three palmprint databases, the Hong Kong Polytechnic University (PolyU) Multispectral Palmprint Database, Hong Kong PolyU Palmprint Database II, and the Delhi Touchless (IIDT) Palmprint Database, are used in this study. The study shows that our method effectively identifies and verifies palmprints and outperforms other methods based on feature extraction.

PROMISE: a tool to identify genomic features with a specific biologically interesting pattern of associations with multiple endpoint variables.

PubMed

Pounds, Stan; Cheng, Cheng; Cao, Xueyuan; Crews, Kristine R; Plunkett, William; Gandhi, Varsha; Rubnitz, Jeffrey; Ribeiro, Raul C; Downing, James R; Lamba, Jatinder

2009-08-15

In some applications, prior biological knowledge can be used to define a specific pattern of association of multiple endpoint variables with a genomic variable that is biologically most interesting. However, to our knowledge, there is no statistical procedure designed to detect specific patterns of association with multiple endpoint variables. Projection onto the most interesting statistical evidence (PROMISE) is proposed as a general procedure to identify genomic variables that exhibit a specific biologically interesting pattern of association with multiple endpoint variables. Biological knowledge of the endpoint variables is used to define a vector that represents the biologically most interesting values for statistics that characterize the associations of the endpoint variables with a genomic variable. A test statistic is defined as the dot-product of the vector of the observed association statistics and the vector of the most interesting values of the association statistics. By definition, this test statistic is proportional to the length of the projection of the observed vector of correlations onto the vector of most interesting associations. Statistical significance is determined via permutation. In simulation studies and an example application, PROMISE shows greater statistical power to identify genes with the interesting pattern of associations than classical multivariate procedures, individual endpoint analyses or listing genes that have the pattern of interest and are significant in more than one individual endpoint analysis. Documented R routines are freely available from www.stjuderesearch.org/depts/biostats and will soon be available as a Bioconductor package from www.bioconductor.org.

Plaque echodensity and textural features are associated with histologic carotid plaque instability.

PubMed

Doonan, Robert J; Gorgui, Jessica; Veinot, Jean P; Lai, Chi; Kyriacou, Efthyvoulos; Corriveau, Marc M; Steinmetz, Oren K; Daskalopoulou, Stella S

2016-09-01

Carotid plaque echodensity and texture features predict cerebrovascular symptomatology. Our purpose was to determine the association of echodensity and textural features obtained from a digital image analysis (DIA) program with histologic features of plaque instability as well as to identify the specific morphologic characteristics of unstable plaques. Patients scheduled to undergo carotid endarterectomy were recruited and underwent carotid ultrasound imaging. DIA was performed to extract echodensity and textural features using Plaque Texture Analysis software (LifeQ Medical Ltd, Nicosia, Cyprus). Carotid plaque surgical specimens were obtained and analyzed histologically. Principal component analysis (PCA) was performed to reduce imaging variables. Logistic regression models were used to determine if PCA variables and individual imaging variables predicted histologic features of plaque instability. Image analysis data from 160 patients were analyzed. Individual imaging features of plaque echolucency and homogeneity were associated with a more unstable plaque phenotype on histology. These results were independent of age, sex, and degree of carotid stenosis. PCA reduced 39 individual imaging variables to five PCA variables. PCA1 and PCA2 were significantly associated with overall plaque instability on histology (both P = .02), whereas PCA3 did not achieve statistical significance (P = .07). DIA features of carotid plaques are associated with histologic plaque instability as assessed by multiple histologic features. Importantly, unstable plaques on histology appear more echolucent and homogeneous on ultrasound imaging. These results are independent of stenosis, suggesting that image analysis may have a role in refining the selection of patients who undergo carotid endarterectomy. Copyright © 2016 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

Damage detection of engine bladed-disks using multivariate statistical analysis

NASA Astrophysics Data System (ADS)

Fang, X.; Tang, J.

2006-03-01

The timely detection of damage in aero-engine bladed-disks is an extremely important and challenging research topic. Bladed-disks have high modal density and, particularly, their vibration responses are subject to significant uncertainties due to manufacturing tolerance (blade-to-blade difference or mistuning), operating condition change and sensor noise. In this study, we present a new methodology for the on-line damage detection of engine bladed-disks using their vibratory responses during spin-up or spin-down operations which can be measured by blade-tip-timing sensing technique. We apply a principle component analysis (PCA)-based approach for data compression, feature extraction, and denoising. The non-model based damage detection is achieved by analyzing the change between response features of the healthy structure and of the damaged one. We facilitate such comparison by incorporating the Hotelling's statistic T2 analysis, which yields damage declaration with a given confidence level. The effectiveness of the method is demonstrated by case studies.

Evaluating SPLASH-2 Applications Using MapReduce

NASA Astrophysics Data System (ADS)

Zhu, Shengkai; Xiao, Zhiwei; Chen, Haibo; Chen, Rong; Zhang, Weihua; Zang, Binyu

MapReduce has been prevalent for running data-parallel applications. By hiding other non-functionality parts such as parallelism, fault tolerance and load balance from programmers, MapReduce significantly simplifies the programming of large clusters. Due to the mentioned features of MapReduce above, researchers have also explored the use of MapReduce on other application domains, such as machine learning, textual retrieval and statistical translation, among others.

On the structure and phase transitions of power-law Poissonian ensembles

NASA Astrophysics Data System (ADS)

Eliazar, Iddo; Oshanin, Gleb

2012-10-01

Power-law Poissonian ensembles are Poisson processes that are defined on the positive half-line, and that are governed by power-law intensities. Power-law Poissonian ensembles are stochastic objects of fundamental significance; they uniquely display an array of fractal features and they uniquely generate a span of important applications. In this paper we apply three different methods—oligarchic analysis, Lorenzian analysis and heterogeneity analysis—to explore power-law Poissonian ensembles. The amalgamation of these analyses, combined with the topology of power-law Poissonian ensembles, establishes a detailed and multi-faceted picture of the statistical structure and the statistical phase transitions of these elemental ensembles.

Machine Learning Approach for Classifying Multiple Sclerosis Courses by Combining Clinical Data with Lesion Loads and Magnetic Resonance Metabolic Features.

PubMed

Ion-Mărgineanu, Adrian; Kocevar, Gabriel; Stamile, Claudio; Sima, Diana M; Durand-Dubief, Françoise; Van Huffel, Sabine; Sappey-Marinier, Dominique

2017-01-01

Purpose: The purpose of this study is classifying multiple sclerosis (MS) patients in the four clinical forms as defined by the McDonald criteria using machine learning algorithms trained on clinical data combined with lesion loads and magnetic resonance metabolic features. Materials and Methods: Eighty-seven MS patients [12 Clinically Isolated Syndrome (CIS), 30 Relapse Remitting (RR), 17 Primary Progressive (PP), and 28 Secondary Progressive (SP)] and 18 healthy controls were included in this study. Longitudinal data available for each MS patient included clinical (e.g., age, disease duration, Expanded Disability Status Scale), conventional magnetic resonance imaging and spectroscopic imaging. We extract N -acetyl-aspartate (NAA), Choline (Cho), and Creatine (Cre) concentrations, and we compute three features for each spectroscopic grid by averaging metabolite ratios (NAA/Cho, NAA/Cre, Cho/Cre) over good quality voxels. We built linear mixed-effects models to test for statistically significant differences between MS forms. We test nine binary classification tasks on clinical data, lesion loads, and metabolic features, using a leave-one-patient-out cross-validation method based on 100 random patient-based bootstrap selections. We compute F1-scores and BAR values after tuning Linear Discriminant Analysis (LDA), Support Vector Machines with gaussian kernel (SVM-rbf), and Random Forests. Results: Statistically significant differences were found between the disease starting points of each MS form using four different response variables: Lesion Load, NAA/Cre, NAA/Cho, and Cho/Cre ratios. Training SVM-rbf on clinical and lesion loads yields F1-scores of 71-72% for CIS vs. RR and CIS vs. RR+SP, respectively. For RR vs. PP we obtained good classification results (maximum F1-score of 85%) after training LDA on clinical and metabolic features, while for RR vs. SP we obtained slightly higher classification results (maximum F1-score of 87%) after training LDA and SVM-rbf on clinical, lesion loads and metabolic features. Conclusions: Our results suggest that metabolic features are better at differentiating between relapsing-remitting and primary progressive forms, while lesion loads are better at differentiating between relapsing-remitting and secondary progressive forms. Therefore, combining clinical data with magnetic resonance lesion loads and metabolic features can improve the discrimination between relapsing-remitting and progressive forms.

Computer-aided assessment of breast density: comparison of supervised deep learning and feature-based statistical learning.

PubMed

Li, Songfeng; Wei, Jun; Chan, Heang-Ping; Helvie, Mark A; Roubidoux, Marilyn A; Lu, Yao; Zhou, Chuan; Hadjiiski, Lubomir M; Samala, Ravi K

2018-01-09

Breast density is one of the most significant factors that is associated with cancer risk. In this study, our purpose was to develop a supervised deep learning approach for automated estimation of percentage density (PD) on digital mammograms (DMs). The input 'for processing' DMs was first log-transformed, enhanced by a multi-resolution preprocessing scheme, and subsampled to a pixel size of 800 µm  ×  800 µm from 100 µm  ×  100 µm. A deep convolutional neural network (DCNN) was trained to estimate a probability map of breast density (PMD) by using a domain adaptation resampling method. The PD was estimated as the ratio of the dense area to the breast area based on the PMD. The DCNN approach was compared to a feature-based statistical learning approach. Gray level, texture and morphological features were extracted and a least absolute shrinkage and selection operator was used to combine the features into a feature-based PMD. With approval of the Institutional Review Board, we retrospectively collected a training set of 478 DMs and an independent test set of 183 DMs from patient files in our institution. Two experienced mammography quality standards act radiologists interactively segmented PD as the reference standard. Ten-fold cross-validation was used for model selection and evaluation with the training set. With cross-validation, DCNN obtained a Dice's coefficient (DC) of 0.79  ±  0.13 and Pearson's correlation (r) of 0.97, whereas feature-based learning obtained DC  =  0.72  ±  0.18 and r  =  0.85. For the independent test set, DCNN achieved DC  =  0.76  ±  0.09 and r  =  0.94, while feature-based learning achieved DC  =  0.62  ±  0.21 and r  =  0.75. Our DCNN approach was significantly better and more robust than the feature-based learning approach for automated PD estimation on DMs, demonstrating its potential use for automated density reporting as well as for model-based risk prediction.

HCS-Neurons: identifying phenotypic changes in multi-neuron images upon drug treatments of high-content screening.

PubMed

Charoenkwan, Phasit; Hwang, Eric; Cutler, Robert W; Lee, Hua-Chin; Ko, Li-Wei; Huang, Hui-Ling; Ho, Shinn-Ying

2013-01-01

High-content screening (HCS) has become a powerful tool for drug discovery. However, the discovery of drugs targeting neurons is still hampered by the inability to accurately identify and quantify the phenotypic changes of multiple neurons in a single image (named multi-neuron image) of a high-content screen. Therefore, it is desirable to develop an automated image analysis method for analyzing multi-neuron images. We propose an automated analysis method with novel descriptors of neuromorphology features for analyzing HCS-based multi-neuron images, called HCS-neurons. To observe multiple phenotypic changes of neurons, we propose two kinds of descriptors which are neuron feature descriptor (NFD) of 13 neuromorphology features, e.g., neurite length, and generic feature descriptors (GFDs), e.g., Haralick texture. HCS-neurons can 1) automatically extract all quantitative phenotype features in both NFD and GFDs, 2) identify statistically significant phenotypic changes upon drug treatments using ANOVA and regression analysis, and 3) generate an accurate classifier to group neurons treated by different drug concentrations using support vector machine and an intelligent feature selection method. To evaluate HCS-neurons, we treated P19 neurons with nocodazole (a microtubule depolymerizing drug which has been shown to impair neurite development) at six concentrations ranging from 0 to 1000 ng/mL. The experimental results show that all the 13 features of NFD have statistically significant difference with respect to changes in various levels of nocodazole drug concentrations (NDC) and the phenotypic changes of neurites were consistent to the known effect of nocodazole in promoting neurite retraction. Three identified features, total neurite length, average neurite length, and average neurite area were able to achieve an independent test accuracy of 90.28% for the six-dosage classification problem. This NFD module and neuron image datasets are provided as a freely downloadable MatLab project at http://iclab.life.nctu.edu.tw/HCS-Neurons. Few automatic methods focus on analyzing multi-neuron images collected from HCS used in drug discovery. We provided an automatic HCS-based method for generating accurate classifiers to classify neurons based on their phenotypic changes upon drug treatments. The proposed HCS-neurons method is helpful in identifying and classifying chemical or biological molecules that alter the morphology of a group of neurons in HCS.

Computer-aided assessment of breast density: comparison of supervised deep learning and feature-based statistical learning

NASA Astrophysics Data System (ADS)

Li, Songfeng; Wei, Jun; Chan, Heang-Ping; Helvie, Mark A.; Roubidoux, Marilyn A.; Lu, Yao; Zhou, Chuan; Hadjiiski, Lubomir M.; Samala, Ravi K.

2018-01-01

Breast density is one of the most significant factors that is associated with cancer risk. In this study, our purpose was to develop a supervised deep learning approach for automated estimation of percentage density (PD) on digital mammograms (DMs). The input ‘for processing’ DMs was first log-transformed, enhanced by a multi-resolution preprocessing scheme, and subsampled to a pixel size of 800 µm  ×  800 µm from 100 µm  ×  100 µm. A deep convolutional neural network (DCNN) was trained to estimate a probability map of breast density (PMD) by using a domain adaptation resampling method. The PD was estimated as the ratio of the dense area to the breast area based on the PMD. The DCNN approach was compared to a feature-based statistical learning approach. Gray level, texture and morphological features were extracted and a least absolute shrinkage and selection operator was used to combine the features into a feature-based PMD. With approval of the Institutional Review Board, we retrospectively collected a training set of 478 DMs and an independent test set of 183 DMs from patient files in our institution. Two experienced mammography quality standards act radiologists interactively segmented PD as the reference standard. Ten-fold cross-validation was used for model selection and evaluation with the training set. With cross-validation, DCNN obtained a Dice’s coefficient (DC) of 0.79  ±  0.13 and Pearson’s correlation (r) of 0.97, whereas feature-based learning obtained DC  =  0.72  ±  0.18 and r  =  0.85. For the independent test set, DCNN achieved DC  =  0.76  ±  0.09 and r  =  0.94, while feature-based learning achieved DC  =  0.62  ±  0.21 and r  =  0.75. Our DCNN approach was significantly better and more robust than the feature-based learning approach for automated PD estimation on DMs, demonstrating its potential use for automated density reporting as well as for model-based risk prediction.

Early Assessment of Treatment Responses During Radiation Therapy for Lung Cancer Using Quantitative Analysis of Daily Computed Tomography

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

Paul, Jijo; Yang, Cungeng; Wu, Hui

Purpose: To investigate early tumor and normal tissue responses during the course of radiation therapy (RT) for lung cancer using quantitative analysis of daily computed tomography (CT) scans. Methods and Materials: Daily diagnostic-quality CT scans acquired using CT-on-rails during CT-guided RT for 20 lung cancer patients were quantitatively analyzed. On each daily CT set, the contours of the gross tumor volume (GTV) and lungs were generated and the radiation dose delivered was reconstructed. The changes in CT image intensity (Hounsfield unit [HU]) features in the GTV and the multiple normal lung tissue shells around the GTV were extracted from themore » daily CT scans. The associations between the changes in the mean HUs, GTV, accumulated dose during RT delivery, and patient survival rate were analyzed. Results: During the RT course, radiation can induce substantial changes in the HU histogram features on the daily CT scans, with reductions in the GTV mean HUs (dH) observed in the range of 11 to 48 HU (median 30). The dH is statistically related to the accumulated GTV dose (R{sup 2} > 0.99) and correlates weakly with the change in GTV (R{sup 2} = 0.3481). Statistically significant increases in patient survival rates (P=.038) were observed for patients with a higher dH in the GTV. In the normal lung, the 4 regions proximal to the GTV showed statistically significant (P<.001) HU reductions from the first to last fraction. Conclusion: Quantitative analysis of the daily CT scans indicated that the mean HUs in lung tumor and surrounding normal tissue were reduced during RT delivery. This reduction was observed in the early phase of the treatment, is patient specific, and correlated with the delivered dose. A larger HU reduction in the GTV correlated significantly with greater patient survival. The changes in daily CT features, such as the mean HU, can be used for early assessment of the radiation response during RT delivery for lung cancer.« less

Early Assessment of Treatment Responses During Radiation Therapy for Lung Cancer Using Quantitative Analysis of Daily Computed Tomography.

PubMed

Paul, Jijo; Yang, Cungeng; Wu, Hui; Tai, An; Dalah, Entesar; Zheng, Cheng; Johnstone, Candice; Kong, Feng-Ming; Gore, Elizabeth; Li, X Allen

2017-06-01

To investigate early tumor and normal tissue responses during the course of radiation therapy (RT) for lung cancer using quantitative analysis of daily computed tomography (CT) scans. Daily diagnostic-quality CT scans acquired using CT-on-rails during CT-guided RT for 20 lung cancer patients were quantitatively analyzed. On each daily CT set, the contours of the gross tumor volume (GTV) and lungs were generated and the radiation dose delivered was reconstructed. The changes in CT image intensity (Hounsfield unit [HU]) features in the GTV and the multiple normal lung tissue shells around the GTV were extracted from the daily CT scans. The associations between the changes in the mean HUs, GTV, accumulated dose during RT delivery, and patient survival rate were analyzed. During the RT course, radiation can induce substantial changes in the HU histogram features on the daily CT scans, with reductions in the GTV mean HUs (dH) observed in the range of 11 to 48 HU (median 30). The dH is statistically related to the accumulated GTV dose (R 2  > 0.99) and correlates weakly with the change in GTV (R 2  = 0.3481). Statistically significant increases in patient survival rates (P=.038) were observed for patients with a higher dH in the GTV. In the normal lung, the 4 regions proximal to the GTV showed statistically significant (P<.001) HU reductions from the first to last fraction. Quantitative analysis of the daily CT scans indicated that the mean HUs in lung tumor and surrounding normal tissue were reduced during RT delivery. This reduction was observed in the early phase of the treatment, is patient specific, and correlated with the delivered dose. A larger HU reduction in the GTV correlated significantly with greater patient survival. The changes in daily CT features, such as the mean HU, can be used for early assessment of the radiation response during RT delivery for lung cancer. Copyright © 2017 Elsevier Inc. All rights reserved.

Diagnosis of Diabetes Mellitus by Extraction of Morphological Features of Red Blood Cells Using an Artificial Neural Network.

PubMed

Palanisamy, Vinupritha; Mariamichael, Anburajan

2016-10-01

Background and Aim: Diabetes mellitus is a metabolic disorder characterized by varying hyperglycemias either due to insufficient secretion of insulin by the pancreas or improper utilization of glucose. The study was aimed to investigate the association of morphological features of erythrocytes among normal and diabetic subjects and its gender-based changes and thereby to develop a computer aided tool to diagnose diabetes using features extracted from RBC. Materials and Methods: The study involved 138 normal and 144 diabetic subjects. The blood was drawn from the subjects and the blood smear prepared was digitized using Zeiss fluorescent microscope. The digitized images were pre-processed and texture segmentation was performed to extract the various morphological features. The Pearson correlation test was performed and subsequently, classification of subjects as normal and diabetes was carried out by a neural network classifier based on the features that demonstrated significance at the level of P <0.05. Result: The proposed system demonstrated an overall accuracy, sensitivity, specificity, positive predictive value and negative predictive value of 93.3, 93.71, 92.8, 93.1 and 93.5% respectively. Conclusion: The morphological features exhibited a statistically significant difference (P<0.01) between the normal and diabetic cells, suggesting that it could be helpful in the diagnosis of Diabetes mellitus using a computer aided system. © Georg Thieme Verlag KG Stuttgart · New York.

Quantitative analysis of breast cancer diagnosis using a probabilistic modelling approach.

PubMed

Liu, Shuo; Zeng, Jinshu; Gong, Huizhou; Yang, Hongqin; Zhai, Jia; Cao, Yi; Liu, Junxiu; Luo, Yuling; Li, Yuhua; Maguire, Liam; Ding, Xuemei

2018-01-01

Breast cancer is the most prevalent cancer in women in most countries of the world. Many computer-aided diagnostic methods have been proposed, but there are few studies on quantitative discovery of probabilistic dependencies among breast cancer data features and identification of the contribution of each feature to breast cancer diagnosis. This study aims to fill this void by utilizing a Bayesian network (BN) modelling approach. A K2 learning algorithm and statistical computation methods are used to construct BN structure and assess the obtained BN model. The data used in this study were collected from a clinical ultrasound dataset derived from a Chinese local hospital and a fine-needle aspiration cytology (FNAC) dataset from UCI machine learning repository. Our study suggested that, in terms of ultrasound data, cell shape is the most significant feature for breast cancer diagnosis, and the resistance index presents a strong probabilistic dependency on blood signals. With respect to FNAC data, bare nuclei are the most important discriminating feature of malignant and benign breast tumours, and uniformity of both cell size and cell shape are tightly interdependent. The BN modelling approach can support clinicians in making diagnostic decisions based on the significant features identified by the model, especially when some other features are missing for specific patients. The approach is also applicable to other healthcare data analytics and data modelling for disease diagnosis. Copyright © 2017 Elsevier Ltd. All rights reserved.

Morphometric features of the right atrioventricular orifice in adult human hearts.

PubMed

Skwarek, M; Hreczecha, J; Dudziak, M; Jerzemowski, J; Szpinda, M; Grzybiak, M

2008-02-01

The normal data of the tricuspid valve complex is of great clinical importance in the light of progress in cardiosurgery and the development of novel operating techniques. A range of measurements for the right atrioventricular orifice in 96 human adult hearts was examined by means of anatomical dissection, inspection, examination, and statistical analyses. The length of the attachment of the anterior leaflet increased significantly between group I (aged 18-40 years) and group II (aged 41-64 years) in women only. In men there were no significant differences in this parameter between any of the three age groups. In addition, the attachment length of the posterior leaflet in women increased statistically in the second age group. In men, in contrast, the attachment length of the posterior leaflet did not increase significantly between the first and second age groups and became significantly larger only in oldest age group, consisting of men aged over 65. No statistically significant differences between the three age groups were found for the attachment length of the septal leaflet (p>0.05). In female hearts significant increases in the frontal and sagittal dimensions of the tricuspid valve orifice were observed between the second age group and the group aged over 65. In male hearts both the frontal and sagittal dimensions increased significantly with advanced age. The right atrioventricular orifice expressed as the ellipse area was statistically greater than the triangular area (p<0.01) in each age group. It should be noticed that both areas increased significantly during ageing. This study has demonstrated that the shape of the right atrioventricular orifice evolves during life, from a triangular shape to a more elliptical shape.

A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm

PubMed Central

Zhang, Xin; Cui, Jintian; Wang, Weisheng; Lin, Chao

2017-01-01

To address the problem of image texture feature extraction, a direction measure statistic that is based on the directionality of image texture is constructed, and a new method of texture feature extraction, which is based on the direction measure and a gray level co-occurrence matrix (GLCM) fusion algorithm, is proposed in this paper. This method applies the GLCM to extract the texture feature value of an image and integrates the weight factor that is introduced by the direction measure to obtain the final texture feature of an image. A set of classification experiments for the high-resolution remote sensing images were performed by using support vector machine (SVM) classifier with the direction measure and gray level co-occurrence matrix fusion algorithm. Both qualitative and quantitative approaches were applied to assess the classification results. The experimental results demonstrated that texture feature extraction based on the fusion algorithm achieved a better image recognition, and the accuracy of classification based on this method has been significantly improved. PMID:28640181

Keratoacanthoma versus invasive squamous cell carcinoma: a comparison of dermatoscopic vascular features in 510 cases.

PubMed

Pyne, John H; Windrum, Graham; Sapkota, Devendra; Wong, Jian Cheng

2014-07-01

Keratoacanthoma (KA) and invasive squamous cell carcinoma (SCC) are keratinocytic tumors displaying vascular features, imaged using dermatoscopy. Compare the dermatoscopy vascular features of KA to SCC. This prospective study examined consecutive cases of 100 KA and 410 invasive SCC in a single private practice in Sydney, Australia. Vascular features were recorded in vivo direct from patients using a non-polarized Delta 20 Heine dermatoscope. These vascular features were: linear, branching, serpentine, hairpin, glomerular and dot vessels, the presence or absence of large diameter tumor vessels, vessel presence in central verses peripheral tumor areas and tumor pink areas in different proportions. Following full excision, all cases were submitted for histopathologic diagnosis. Branching vessels were the only vessel morphology that varied, with a significant incidence in KA (25.0%), compared to SCC (10.7%), P < 0.01. Large vessels were identified in 20.0% of KA, compared to 12.4% in SCC, P = 0.05. No vessels were observed in the central tumor areas in 43.4 % of KA compared to 58.0% of SCC, P = 0.01. Other data comparing the central versus peripheral tumor areas for vessels present did not reveal any distinctive associations. There were no significant differences between KA and SCC when reviewing the selected proportions of pink within the tumor. The vascular features may be confounded by tumor depth in KA. Polarized dermatoscopy may not produce the same findings. This study found branching vessels to have a higher incidence in KA compared to invasive SCC. Although not statistically significant, large diameter vessels were also more frequent in KA. Proportions of pink within the tumor or central verses peripheral tumor vessel distribution were not useful diagnostic features separating KA from SCC using dermatoscopy.

Contrasting effects of feature-based statistics on the categorisation and identification of visual objects

PubMed Central

Taylor, Kirsten I.; Devereux, Barry J.; Acres, Kadia; Randall, Billi; Tyler, Lorraine K.

2013-01-01

Conceptual representations are at the heart of our mental lives, involved in every aspect of cognitive functioning. Despite their centrality, a long-standing debate persists as to how the meanings of concepts are represented and processed. Many accounts agree that the meanings of concrete concepts are represented by their individual features, but disagree about the importance of different feature-based variables: some views stress the importance of the information carried by distinctive features in conceptual processing, others the features which are shared over many concepts, and still others the extent to which features co-occur. We suggest that previously disparate theoretical positions and experimental findings can be unified by an account which claims that task demands determine how concepts are processed in addition to the effects of feature distinctiveness and co-occurrence. We tested these predictions in a basic-level naming task which relies on distinctive feature information (Experiment 1) and a domain decision task which relies on shared feature information (Experiment 2). Both used large-scale regression designs with the same visual objects, and mixed-effects models incorporating participant, session, stimulus-related and feature statistic variables to model the performance. We found that concepts with relatively more distinctive and more highly correlated distinctive relative to shared features facilitated basic-level naming latencies, while concepts with relatively more shared and more highly correlated shared relative to distinctive features speeded domain decisions. These findings demonstrate that the feature statistics of distinctiveness (shared vs. distinctive) and correlational strength, as well as the task demands, determine how concept meaning is processed in the conceptual system. PMID:22137770

Entropy, pricing and productivity of pumped-storage

NASA Astrophysics Data System (ADS)

Karakatsanis, Georgios; Tyralis, Hristos; Tzouka, Katerina

2016-04-01

Pumped-storage constitutes today a mature method of bulk electricity storage in the form of hydropower. This bulk electricity storability upgrades the economic value of hydropower as it may mitigate -or even neutralize- stochastic effects deriving from various geophysical and socioeconomic factors, which produce numerous load balance inefficiencies due to increased uncertainty. Pumped-storage further holds a key role for unifying intermittent renewable (i.e. wind, solar) units with controllable non-renewable (i.e. nuclear, coal) fuel electricity generation plants into integrated energy systems. We develop a set of indicators for the measurement of performance of pumped-storage, in terms of the latter's energy and financial contribution to the energy system. More specifically, we use the concept of entropy in order to examine: (1) the statistical features -and correlations- of the energy system's intermittent components and (2) the statistical features of electricity demand prediction deviations. In this way, the macroeconomics of pumped-storage emerges naturally from its statistical features (Karakatsanis et al. 2014). In addition, these findings are combined to actual daily loads. Hence, not only the amount of energy harvested from the pumped-storage component is expected to be important, but the harvesting time as well, as the intraday price of electricity varies significantly. Additionally, the structure of the pumped-storage market proves to be a significant factor as well for the system's energy and financial performance (Paine et al. 2014). According to the above, we aim at postulating a set of general rules on the productivity of pumped-storage for (integrated) energy systems. Keywords: pumped-storage, storability, economic value of hydropower, stochastic effects, uncertainty, energy systems, entropy, intraday electricity price, productivity References 1. Karakatsanis, Georgios et al. (2014), Entropy, pricing and macroeconomics of pumped-storage systems, Vienna, Austria, April 27 - May 2 2014, "The Face of the Earth - Process and Form", European Geophysical Union General Assembly 2. Paine, Nathan et al. (2014), Why market rules matter: Optimizing pumped hydroelectric storage when compensation rules differ, Energy Economics 46, 10-19

High Dimensional Classification Using Features Annealed Independence Rules.

PubMed

Fan, Jianqing; Fan, Yingying

2008-01-01

Classification using high-dimensional features arises frequently in many contemporary statistical studies such as tumor classification using microarray or other high-throughput data. The impact of dimensionality on classifications is largely poorly understood. In a seminal paper, Bickel and Levina (2004) show that the Fisher discriminant performs poorly due to diverging spectra and they propose to use the independence rule to overcome the problem. We first demonstrate that even for the independence classification rule, classification using all the features can be as bad as the random guessing due to noise accumulation in estimating population centroids in high-dimensional feature space. In fact, we demonstrate further that almost all linear discriminants can perform as bad as the random guessing. Thus, it is paramountly important to select a subset of important features for high-dimensional classification, resulting in Features Annealed Independence Rules (FAIR). The conditions under which all the important features can be selected by the two-sample t-statistic are established. The choice of the optimal number of features, or equivalently, the threshold value of the test statistics are proposed based on an upper bound of the classification error. Simulation studies and real data analysis support our theoretical results and demonstrate convincingly the advantage of our new classification procedure.

Analysis of Feature Intervisibility and Cumulative Visibility Using GIS, Bayesian and Spatial Statistics: A Study from the Mandara Mountains, Northern Cameroon

PubMed Central

Wright, David K.; MacEachern, Scott; Lee, Jaeyong

2014-01-01

The locations of diy-geδ-bay (DGB) sites in the Mandara Mountains, northern Cameroon are hypothesized to occur as a function of their ability to see and be seen from points on the surrounding landscape. A series of geostatistical, two-way and Bayesian logistic regression analyses were performed to test two hypotheses related to the intervisibility of the sites to one another and their visual prominence on the landscape. We determine that the intervisibility of the sites to one another is highly statistically significant when compared to 10 stratified-random permutations of DGB sites. Bayesian logistic regression additionally demonstrates that the visibility of the sites to points on the surrounding landscape is statistically significant. The location of sites appears to have also been selected on the basis of lower slope than random permutations of sites. Using statistical measures, many of which are not commonly employed in archaeological research, to evaluate aspects of visibility on the landscape, we conclude that the placement of DGB sites improved their conspicuousness for enhanced ritual, social cooperation and/or competition purposes. PMID:25383883

High-Performance Computational Analysis of Glioblastoma Pathology Images with Database Support Identifies Molecular and Survival Correlates.

PubMed

Kong, Jun; Wang, Fusheng; Teodoro, George; Cooper, Lee; Moreno, Carlos S; Kurc, Tahsin; Pan, Tony; Saltz, Joel; Brat, Daniel

2013-12-01

In this paper, we present a novel framework for microscopic image analysis of nuclei, data management, and high performance computation to support translational research involving nuclear morphometry features, molecular data, and clinical outcomes. Our image analysis pipeline consists of nuclei segmentation and feature computation facilitated by high performance computing with coordinated execution in multi-core CPUs and Graphical Processor Units (GPUs). All data derived from image analysis are managed in a spatial relational database supporting highly efficient scientific queries. We applied our image analysis workflow to 159 glioblastomas (GBM) from The Cancer Genome Atlas dataset. With integrative studies, we found statistics of four specific nuclear features were significantly associated with patient survival. Additionally, we correlated nuclear features with molecular data and found interesting results that support pathologic domain knowledge. We found that Proneural subtype GBMs had the smallest mean of nuclear Eccentricity and the largest mean of nuclear Extent, and MinorAxisLength. We also found gene expressions of stem cell marker MYC and cell proliferation maker MKI67 were correlated with nuclear features. To complement and inform pathologists of relevant diagnostic features, we queried the most representative nuclear instances from each patient population based on genetic and transcriptional classes. Our results demonstrate that specific nuclear features carry prognostic significance and associations with transcriptional and genetic classes, highlighting the potential of high throughput pathology image analysis as a complementary approach to human-based review and translational research.

SU-E-I-85: Exploring the 18F-Fluorodeoxyglucose PET Characteristics in Staging of Esophageal Squamous Cell Carcinoma

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

Ma, C; Yin, Y

2014-06-01

Purpose: The aim of this study was to explore the characteristics derived from 18F-fluorodeoxyglucose (18F-FDG) PET image and assess its capacity in staging of esophageal squamous cell carcinoma (ESCC). Methods: 26 patients with newly diagnosed ESCC who underwent 18F-FDG PET scan were included in this study. Different image-derived indices including the standardized uptake value (SUV), gross tumor length, texture features and shape feature were considered. Taken the histopathologic examination as the gold standard, the extracted capacities of indices in staging of ESCC were assessed by Kruskal-Wallis test and Mann-Whitney test. Specificity and sensitivity for each of the studied parameters weremore » derived using receiver-operating characteristic curves. Results: 18F-FDG SUVmax and SUVmean showed statistically significant capability in AJCC and TNM stages. Texture features such as ENT and CORR were significant factors for N stages(p=0.040, p=0.029). Both FDG PET Longitudinal length and shape feature Eccentricity (EC) (p≤0.010) provided powerful stratification in the primary ESCC AJCC and TNM stages than SUV and texture features. Receiver-operating-characteristic curve analysis showed that tumor textural analysis can capability M stages with higher sensitivity than SUV measurement but lower in T and N stages. Conclusion: The 18F-FDG image-derived characteristics of SUV, textural features and shape feature allow for good stratification AJCC and TNM stage in ESCC patients.« less

VARSEDIG: an algorithm for morphometric characters selection and statistical validation in morphological taxonomy.

PubMed

Guisande, Cástor; Vari, Richard P; Heine, Jürgen; García-Roselló, Emilio; González-Dacosta, Jacinto; Perez-Schofield, Baltasar J García; González-Vilas, Luis; Pelayo-Villamil, Patricia

2016-09-12

We present and discuss VARSEDIG, an algorithm which identifies the morphometric features that significantly discriminate two taxa and validates the morphological distinctness between them via a Monte-Carlo test. VARSEDIG is freely available as a function of the RWizard application PlotsR (http://www.ipez.es/RWizard) and as R package on CRAN. The variables selected by VARSEDIG with the overlap method were very similar to those selected by logistic regression and discriminant analysis, but overcomes some shortcomings of these methods. VARSEDIG is, therefore, a good alternative by comparison to current classical classification methods for identifying morphometric features that significantly discriminate a taxon and for validating its morphological distinctness from other taxa. As a demonstration of the potential of VARSEDIG for this purpose, we analyze morphological discrimination among some species of the Neotropical freshwater family Characidae.

A study of the utilization of ERTS-1 data from the Wabash River Basin

NASA Technical Reports Server (NTRS)

Landgrebe, D. A. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Nine projects are defined, five ERTS data applications experiments and four supporting technology tasks. The most significant applications results were achieved in the soil association mapping, earth surface feature identification, and urban land use mapping efforts. Four soil association boundaries were accurately delineated from ERTS-1 imagery. A data bank has been developed to test surface feature classifications obtained from ERTS-1 data. Preliminary forest cover classifications indicated that the number of acres estimated tended to be greater than actually existed by 25%. Urban land use analysis of ERTS-1 data indicated highly accurate classification could be obtained for many urban catagories. The wooded residential category tended to be misclassified as woods or agricultural land. Further statistical analysis revealed that these classes could be separated using sample variance.

Effect of various binning methods and ROI sizes on the accuracy of the automatic classification system for differentiation between diffuse infiltrative lung diseases on the basis of texture features at HRCT

NASA Astrophysics Data System (ADS)

Kim, Namkug; Seo, Joon Beom; Sung, Yu Sub; Park, Bum-Woo; Lee, Youngjoo; Park, Seong Hoon; Lee, Young Kyung; Kang, Suk-Ho

2008-03-01

To find optimal binning, variable binning size linear binning (LB) and non-linear binning (NLB) methods were tested. In case of small binning size (Q <= 10), NLB shows significant better accuracy than the LB. K-means NLB (Q = 26) is statistically significant better than every LB. To find optimal binning method and ROI size of the automatic classification system for differentiation between diffuse infiltrative lung diseases on the basis of textural analysis at HRCT Six-hundred circular regions of interest (ROI) with 10, 20, and 30 pixel diameter, comprising of each 100 ROIs representing six regional disease patterns (normal, NL; ground-glass opacity, GGO; reticular opacity, RO; honeycombing, HC; emphysema, EMPH; and consolidation, CONS) were marked by an experienced radiologist from HRCT images. Histogram (mean) and co-occurrence matrix (mean and SD of angular second moment, contrast, correlation, entropy, and inverse difference momentum) features were employed to test binning and ROI effects. To find optimal binning, variable binning size LB (bin size Q: 4~30, 32, 64, 128, 144, 196, 256, 384) and NLB (Q: 4~30) methods (K-means, and Fuzzy C-means clustering) were tested. For automated classification, a SVM classifier was implemented. To assess cross-validation of the system, a five-folding method was used. Each test was repeatedly performed twenty times. Overall accuracies with every combination of variable ROIs, and binning sizes were statistically compared. In case of small binning size (Q <= 10), NLB shows significant better accuracy than the LB. K-means NLB (Q = 26) is statistically significant better than every LB. In case of 30x30 ROI size and most of binning size, the K-means method showed better than other NLB and LB methods. When optimal binning and other parameters were set, overall sensitivity of the classifier was 92.85%. The sensitivity and specificity of the system for each class were as follows: NL, 95%, 97.9%; GGO, 80%, 98.9%; RO 85%, 96.9%; HC, 94.7%, 97%; EMPH, 100%, 100%; and CONS, 100%, 100%, respectively. We determined the optimal binning method and ROI size of the automatic classification system for differentiation between diffuse infiltrative lung diseases on the basis of texture features at HRCT.

Determining Image Processing Features Describing the Appearance of Challenging Mitotic Figures and Miscounted Nonmitotic Objects

PubMed Central

Gandomkar, Ziba; Brennan, Patrick C.; Mello-Thoms, Claudia

2017-01-01

Context: Previous studies showed that the agreement among pathologists in recognition of mitoses in breast slides is fairly modest. Aims: Determining the significantly different quantitative features among easily identifiable mitoses, challenging mitoses, and miscounted nonmitoses within breast slides and identifying which color spaces capture the difference among groups better than others. Materials and Methods: The dataset contained 453 mitoses and 265 miscounted objects in breast slides. The mitoses were grouped into three categories based on the confidence degree of three pathologists who annotated them. The mitoses annotated as “probably a mitosis” by the majority of pathologists were considered as the challenging category. The miscounted objects were recognized as a mitosis or probably a mitosis by only one of the pathologists. The mitoses were segmented using k-means clustering, followed by morphological operations. Morphological, intensity-based, and textural features were extracted from the segmented area and also the image patch of 63 × 63 pixels in different channels of eight color spaces. Holistic features describing the mitoses' surrounding cells of each image were also extracted. Statistical Analysis Used: The Kruskal–Wallis H-test followed by the Tukey-Kramer test was used to identify significantly different features. Results: The results indicated that challenging mitoses were smaller and rounder compared to other mitoses. Among different features, the Gabor textural features differed more than others between challenging mitoses and the easily identifiable ones. Sizes of the non-mitoses were similar to easily identifiable mitoses, but nonmitoses were rounder. The intensity-based features from chromatin channels were the most discriminative features between the easily identifiable mitoses and the miscounted objects. Conclusions: Quantitative features can be used to describe the characteristics of challenging mitoses and miscounted nonmitotic objects. PMID:28966834

Identification of sequence motifs significantly associated with antisense activity.

PubMed

McQuisten, Kyle A; Peek, Andrew S

2007-06-07

Predicting the suppression activity of antisense oligonucleotide sequences is the main goal of the rational design of nucleic acids. To create an effective predictive model, it is important to know what properties of an oligonucleotide sequence associate significantly with antisense activity. Also, for the model to be efficient we must know what properties do not associate significantly and can be omitted from the model. This paper will discuss the results of a randomization procedure to find motifs that associate significantly with either high or low antisense suppression activity, analysis of their properties, as well as the results of support vector machine modelling using these significant motifs as features. We discovered 155 motifs that associate significantly with high antisense suppression activity and 202 motifs that associate significantly with low suppression activity. The motifs range in length from 2 to 5 bases, contain several motifs that have been previously discovered as associating highly with antisense activity, and have thermodynamic properties consistent with previous work associating thermodynamic properties of sequences with their antisense activity. Statistical analysis revealed no correlation between a motif's position within an antisense sequence and that sequences antisense activity. Also, many significant motifs existed as subwords of other significant motifs. Support vector regression experiments indicated that the feature set of significant motifs increased correlation compared to all possible motifs as well as several subsets of the significant motifs. The thermodynamic properties of the significantly associated motifs support existing data correlating the thermodynamic properties of the antisense oligonucleotide with antisense efficiency, reinforcing our hypothesis that antisense suppression is strongly associated with probe/target thermodynamics, as there are no enzymatic mediators to speed the process along like the RNA Induced Silencing Complex (RISC) in RNAi. The independence of motif position and antisense activity also allows us to bypass consideration of this feature in the modelling process, promoting model efficiency and reducing the chance of overfitting when predicting antisense activity. The increase in SVR correlation with significant features compared to nearest-neighbour features indicates that thermodynamics alone is likely not the only factor in determining antisense efficiency.

Application of wavelet techniques for cancer diagnosis using ultrasound images: A Review.

PubMed

Sudarshan, Vidya K; Mookiah, Muthu Rama Krishnan; Acharya, U Rajendra; Chandran, Vinod; Molinari, Filippo; Fujita, Hamido; Ng, Kwan Hoong

2016-02-01

Ultrasound is an important and low cost imaging modality used to study the internal organs of human body and blood flow through blood vessels. It uses high frequency sound waves to acquire images of internal organs. It is used to screen normal, benign and malignant tissues of various organs. Healthy and malignant tissues generate different echoes for ultrasound. Hence, it provides useful information about the potential tumor tissues that can be analyzed for diagnostic purposes before therapeutic procedures. Ultrasound images are affected with speckle noise due to an air gap between the transducer probe and the body. The challenge is to design and develop robust image preprocessing, segmentation and feature extraction algorithms to locate the tumor region and to extract subtle information from isolated tumor region for diagnosis. This information can be revealed using a scale space technique such as the Discrete Wavelet Transform (DWT). It decomposes an image into images at different scales using low pass and high pass filters. These filters help to identify the detail or sudden changes in intensity in the image. These changes are reflected in the wavelet coefficients. Various texture, statistical and image based features can be extracted from these coefficients. The extracted features are subjected to statistical analysis to identify the significant features to discriminate normal and malignant ultrasound images using supervised classifiers. This paper presents a review of wavelet techniques used for preprocessing, segmentation and feature extraction of breast, thyroid, ovarian and prostate cancer using ultrasound images. Copyright © 2015 Elsevier Ltd. All rights reserved.

Features of Heart Rate Variability Capture Regulatory Changes During Kangaroo Care in Preterm Infants.

PubMed

Kommers, Deedee R; Joshi, Rohan; van Pul, Carola; Atallah, Louis; Feijs, Loe; Oei, Guid; Bambang Oetomo, Sidarto; Andriessen, Peter

2017-03-01

To determine whether heart rate variability (HRV) can serve as a surrogate measure to track regulatory changes during kangaroo care, a period of parental coregulation distinct from regulation within the incubator. Nurses annotated the starting and ending times of kangaroo care for 3 months. The pre-kangaroo care, during-kangaroo care, and post-kangaroo care data were retrieved in infants with at least 10 accurately annotated kangaroo care sessions. Eight HRV features (5 in the time domain and 3 in the frequency domain) were used to visually and statistically compare the pre-kangaroo care and during-kangaroo care periods. Two of these features, capturing the percentage of heart rate decelerations and the extent of heart rate decelerations, were newly developed for preterm infants. A total of 191 kangaroo care sessions were investigated in 11 preterm infants. Despite clinically irrelevant changes in vital signs, 6 of the 8 HRV features (SD of normal-to-normal intervals, root mean square of the SD, percentage of consecutive normal-to-normal intervals that differ by >50 ms, SD of heart rate decelerations, high-frequency power, and low-frequency/high-frequency ratio) showed a visible and statistically significant difference (P <.01) between stable periods of kangaroo care and pre-kangaroo care. HRV was reduced during kangaroo care owing to a decrease in the extent of transient heart rate decelerations. HRV-based features may be clinically useful for capturing the dynamic changes in autonomic regulation in response to kangaroo care and other changes in environment and state. Copyright © 2016 Elsevier Inc. All rights reserved.

Information processing of motion in facial expression and the geometry of dynamical systems

NASA Astrophysics Data System (ADS)

Assadi, Amir H.; Eghbalnia, Hamid; McMenamin, Brenton W.

2005-01-01

An interesting problem in analysis of video data concerns design of algorithms that detect perceptually significant features in an unsupervised manner, for instance methods of machine learning for automatic classification of human expression. A geometric formulation of this genre of problems could be modeled with help of perceptual psychology. In this article, we outline one approach for a special case where video segments are to be classified according to expression of emotion or other similar facial motions. The encoding of realistic facial motions that convey expression of emotions for a particular person P forms a parameter space XP whose study reveals the "objective geometry" for the problem of unsupervised feature detection from video. The geometric features and discrete representation of the space XP are independent of subjective evaluations by observers. While the "subjective geometry" of XP varies from observer to observer, levels of sensitivity and variation in perception of facial expressions appear to share a certain level of universality among members of similar cultures. Therefore, statistical geometry of invariants of XP for a sample of population could provide effective algorithms for extraction of such features. In cases where frequency of events is sufficiently large in the sample data, a suitable framework could be provided to facilitate the information-theoretic organization and study of statistical invariants of such features. This article provides a general approach to encode motion in terms of a particular genre of dynamical systems and the geometry of their flow. An example is provided to illustrate the general theory.

Lack of significant association between type 2 diabetes mellitus with longitudinal change in diurnal salivary cortisol: the multiethnic study of atherosclerosis.

PubMed

Spanakis, Elias K; Wang, Xu; Sánchez, Brisa N; Diez Roux, Ana V; Needham, Belinda L; Wand, Gary S; Seeman, Teresa; Golden, Sherita Hill

2016-07-01

Cross-sectional association has been shown between type 2 diabetes and hypothalamic-pituitary-adrenal (HPA) axis dysregulation; however, the temporality of this association is unknown. Our aim was to determine if type 2 diabetes is associated with longitudinal change in daily cortisol curve features. We hypothesized that the presence of type 2 diabetes may lead to a more blunted and abnormal HPA axis profile over time, suggestive of increased HPA axis dysregulation. This was a longitudinal cohort study, including 580 community-dwelling individuals (mean age 63.7 ± 9.1 years; 52.8 % women) with (n = 90) and without (n = 490) type 2 diabetes who attended two MultiEthnic Study of Atherosclerosis Stress ancillary study exams separated by 6 years. Outcome measures that were collected were wake-up and bedtime cortisol, cortisol awakening response (CAR), total area under the curve (AUC), and early, late, and overall decline slopes. In univariate analyses, wake-up and AUC increased over 6 years more in persons with as compared to those without type 2 diabetes (11 vs. 7 % increase for wake-up and 17 vs. 11 % for AUC). The early decline slope became flatter over time with a greater flattening observed in diabetic compared to non-diabetic individuals (23 vs. 9 % flatter); however, the change was only statistically significant for wake-up cortisol (p-value: 0.03). Over time, while CAR was reduced more, late decline and overall decline became flatter, and bedtime cortisol increased less in those with as compared to those without type 2 diabetes, none of these changes were statistically significant in adjusted models. We did not identify any statistically significant change in cortisol curve features over 6 years by type 2 diabetes status.

Follicular morphological characteristics may be associated with invasion in follicular thyroid neoplasms with papillary-like nuclear features.

PubMed

Can, Nuray; Celik, Mehmet; Sezer, Yavuz Atakan; Ozyilmaz, Filiz; Ayturk, Semra; Tastekin, Ebru; Sut, Necdet; Gurkan, Hakan; Ustun, Funda; Bulbul, Buket Yilmaz; Guldiken, Sibel; Puyan, Fulya Oz

2017-08-20

The newly proposed nomenclature and diagnostic criteria for encapsulated follicular variant of papillary thyroid carcinoma (EFVPTC), the noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP), could improve the consistency and accuracy of diagnosing this entity. Diagnosis of NIFTP requires evaluation of the complete tumor border or capsule. The presence of tumor invasion in follicular thyroid neoplasms with papillary-like nuclear features has been recently discussed by many authors. In this study, we examined the predictive value and association of follicular morphological characteristics with the tumor invasion. In addition, we analyzed the association between tumor encapsulation and molecular profile in EFVPTC/NIFTP cases. A total of 106 cases of FVPTC were included in the study. The tumors were grouped based on the presence of tumor capsule and characteristics of tumor border, as 1) completely encapsulated tumors without invasion, 2) encapsulated tumors with invasion, 3) infiltrative tumors without a capsule. Clinicopathological features, histomorphological features [nuclear criteria, minor diagnostic features, follicles oriented perpendicular to tumor border/capsule (FOPBC)] and molecular alterations in BRAF, NRAS, and KRAS genes were evaluated. FOPBC were significantly more frequently seen in encapsulated tumors with invasion (p = 0.008). The nuclear features were not associated with the presence of encapsulation and characteristics of tumor border. BRAF mutation was more frequent in infiltrative tumors, while NRAS mutation was more frequent in encapsulated tumors, but the results were not statistically significant (p = 0.917). In conclusion, FOPBC histomorphological feature may be associated with tumor invasion in EFVPTC/NIFTP. Additionally, BRAF/KRAS/NRAS mutation analysis may prevent inadequate treatment in these patients.

A computational visual saliency model based on statistics and machine learning.

PubMed

Lin, Ru-Je; Lin, Wei-Song

2014-08-01

Identifying the type of stimuli that attracts human visual attention has been an appealing topic for scientists for many years. In particular, marking the salient regions in images is useful for both psychologists and many computer vision applications. In this paper, we propose a computational approach for producing saliency maps using statistics and machine learning methods. Based on four assumptions, three properties (Feature-Prior, Position-Prior, and Feature-Distribution) can be derived and combined by a simple intersection operation to obtain a saliency map. These properties are implemented by a similarity computation, support vector regression (SVR) technique, statistical analysis of training samples, and information theory using low-level features. This technique is able to learn the preferences of human visual behavior while simultaneously considering feature uniqueness. Experimental results show that our approach performs better in predicting human visual attention regions than 12 other models in two test databases. © 2014 ARVO.

[Road Extraction in Remote Sensing Images Based on Spectral and Edge Analysis].

PubMed

Zhao, Wen-zhi; Luo, Li-qun; Guo, Zhou; Yue, Jun; Yu, Xue-ying; Liu, Hui; Wei, Jing

2015-10-01

Roads are typically man-made objects in urban areas. Road extraction from high-resolution images has important applications for urban planning and transportation development. However, due to the confusion of spectral characteristic, it is difficult to distinguish roads from other objects by merely using traditional classification methods that mainly depend on spectral information. Edge is an important feature for the identification of linear objects (e. g. , roads). The distribution patterns of edges vary greatly among different objects. It is crucial to merge edge statistical information into spectral ones. In this study, a new method that combines spectral information and edge statistical features has been proposed. First, edge detection is conducted by using self-adaptive mean-shift algorithm on the panchromatic band, which can greatly reduce pseudo-edges and noise effects. Then, edge statistical features are obtained from the edge statistical model, which measures the length and angle distribution of edges. Finally, by integrating the spectral and edge statistical features, SVM algorithm is used to classify the image and roads are ultimately extracted. A series of experiments are conducted and the results show that the overall accuracy of proposed method is 93% comparing with only 78% overall accuracy of the traditional. The results demonstrate that the proposed method is efficient and valuable for road extraction, especially on high-resolution images.

Classification of motor imagery tasks for BCI with multiresolution analysis and multiobjective feature selection.

PubMed

Ortega, Julio; Asensio-Cubero, Javier; Gan, John Q; Ortiz, Andrés

2016-07-15

Brain-computer interfacing (BCI) applications based on the classification of electroencephalographic (EEG) signals require solving high-dimensional pattern classification problems with such a relatively small number of training patterns that curse of dimensionality problems usually arise. Multiresolution analysis (MRA) has useful properties for signal analysis in both temporal and spectral analysis, and has been broadly used in the BCI field. However, MRA usually increases the dimensionality of the input data. Therefore, some approaches to feature selection or feature dimensionality reduction should be considered for improving the performance of the MRA based BCI. This paper investigates feature selection in the MRA-based frameworks for BCI. Several wrapper approaches to evolutionary multiobjective feature selection are proposed with different structures of classifiers. They are evaluated by comparing with baseline methods using sparse representation of features or without feature selection. The statistical analysis, by applying the Kolmogorov-Smirnoff and Kruskal-Wallis tests to the means of the Kappa values evaluated by using the test patterns in each approach, has demonstrated some advantages of the proposed approaches. In comparison with the baseline MRA approach used in previous studies, the proposed evolutionary multiobjective feature selection approaches provide similar or even better classification performances, with significant reduction in the number of features that need to be computed.

Control chart pattern recognition using RBF neural network with new training algorithm and practical features.

PubMed

Addeh, Abdoljalil; Khormali, Aminollah; Golilarz, Noorbakhsh Amiri

2018-05-04

The control chart patterns are the most commonly used statistical process control (SPC) tools to monitor process changes. When a control chart produces an out-of-control signal, this means that the process has been changed. In this study, a new method based on optimized radial basis function neural network (RBFNN) is proposed for control chart patterns (CCPs) recognition. The proposed method consists of four main modules: feature extraction, feature selection, classification and learning algorithm. In the feature extraction module, shape and statistical features are used. Recently, various shape and statistical features have been presented for the CCPs recognition. In the feature selection module, the association rules (AR) method has been employed to select the best set of the shape and statistical features. In the classifier section, RBFNN is used and finally, in RBFNN, learning algorithm has a high impact on the network performance. Therefore, a new learning algorithm based on the bees algorithm has been used in the learning module. Most studies have considered only six patterns: Normal, Cyclic, Increasing Trend, Decreasing Trend, Upward Shift and Downward Shift. Since three patterns namely Normal, Stratification, and Systematic are very similar to each other and distinguishing them is very difficult, in most studies Stratification and Systematic have not been considered. Regarding to the continuous monitoring and control over the production process and the exact type detection of the problem encountered during the production process, eight patterns have been investigated in this study. The proposed method is tested on a dataset containing 1600 samples (200 samples from each pattern) and the results showed that the proposed method has a very good performance. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

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

Apte, A; Veeraraghavan, H; Oh, J

Purpose: To present an open source and free platform to facilitate radiomics research — The “Radiomics toolbox” in CERR. Method: There is scarcity of open source tools that support end-to-end modeling of image features to predict patient outcomes. The “Radiomics toolbox” strives to fill the need for such a software platform. The platform supports (1) import of various kinds of image modalities like CT, PET, MR, SPECT, US. (2) Contouring tools to delineate structures of interest. (3) Extraction and storage of image based features like 1st order statistics, gray-scale co-occurrence and zonesize matrix based texture features and shape features andmore » (4) Statistical Analysis. Statistical analysis of the extracted features is supported with basic functionality that includes univariate correlations, Kaplan-Meir curves and advanced functionality that includes feature reduction and multivariate modeling. The graphical user interface and the data management are performed with Matlab for the ease of development and readability of code and features for wide audience. Open-source software developed with other programming languages is integrated to enhance various components of this toolbox. For example: Java-based DCM4CHE for import of DICOM, R for statistical analysis. Results: The Radiomics toolbox will be distributed as an open source, GNU copyrighted software. The toolbox was prototyped for modeling Oropharyngeal PET dataset at MSKCC. The analysis will be presented in a separate paper. Conclusion: The Radiomics Toolbox provides an extensible platform for extracting and modeling image features. To emphasize new uses of CERR for radiomics and image-based research, we have changed the name from the “Computational Environment for Radiotherapy Research” to the “Computational Environment for Radiological Research”.« less

Relation of glypican-3 and E-cadherin expressions to clinicopathological features and prognosis of mucinous and non-mucinous colorectal adenocarcinoma.

PubMed

Foda, Abd Al-Rahman Mohammad; Mohammad, Mie Ali; Abdel-Aziz, Azza; El-Hawary, Amira Kamal

2015-06-01

Glypican-3 (GPC3) is a member of the membrane-bound heparin sulfate proteoglycans. E-cadherin is an adhesive receptor that is believed to act as a tumor suppressor gene. Many studies had investigated E-cadherin expressions in colorectal carcinoma (CRC) while only one study had investigated GPC3 expression in CRC. This study aims to investigate expression of GCP3 and E-cadherin in colorectal mucinous carcinoma (MA) and non-mucinous adenocarcinoma (NMA) using manual tissue microarray technique. Tumor tissue specimens are collected from 75 cases of MC and 75 cases of NMA who underwent radical surgery from Jan 2007 to Jan 2012 at the Gastroenterology Centre, Mansoura University, Egypt. Their clinicopathological parameters and survival data were revised and analyzed using established statistical methodologies. High-density manual tissue microarrays were constructed using modified mechanical pencil tip technique and immunohistochemistry for GPC3 and E-cadherin was done. NMA showed higher expression of GPC3 than MA with no statistically significant relation. NMA showed a significantly higher E-cadherin expression than MA. GPC3 and E-cadherin positivity rates were significantly interrelated in NMA, but not in MA, group. In NMA group, there was no significant relation between either GPC3 or E-cadherin expression and the clinicopathological features. In a univariate analysis, neither GPC3 nor E-cadherin expression showed a significant impact on disease-free survival (DFS) or overall survival (OS). GPC3 and E-cadherin expressions are not independent prognostic factors in CRC. However, expressions of both are significantly interrelated in NMA patients, suggesting an excellent interplay between both, in contrast to MA. Further molecular studies are needed to further explore the relationship between GCP3 and E-cadherin in colorectal carcinogenesis.

Predictive capabilities of statistical learning methods for lung nodule malignancy classification using diagnostic image features: an investigation using the Lung Image Database Consortium dataset

NASA Astrophysics Data System (ADS)

Hancock, Matthew C.; Magnan, Jerry F.

2017-03-01

To determine the potential usefulness of quantified diagnostic image features as inputs to a CAD system, we investigate the predictive capabilities of statistical learning methods for classifying nodule malignancy, utilizing the Lung Image Database Consortium (LIDC) dataset, and only employ the radiologist-assigned diagnostic feature values for the lung nodules therein, as well as our derived estimates of the diameter and volume of the nodules from the radiologists' annotations. We calculate theoretical upper bounds on the classification accuracy that is achievable by an ideal classifier that only uses the radiologist-assigned feature values, and we obtain an accuracy of 85.74 (+/-1.14)% which is, on average, 4.43% below the theoretical maximum of 90.17%. The corresponding area-under-the-curve (AUC) score is 0.932 (+/-0.012), which increases to 0.949 (+/-0.007) when diameter and volume features are included, along with the accuracy to 88.08 (+/-1.11)%. Our results are comparable to those in the literature that use algorithmically-derived image-based features, which supports our hypothesis that lung nodules can be classified as malignant or benign using only quantified, diagnostic image features, and indicates the competitiveness of this approach. We also analyze how the classification accuracy depends on specific features, and feature subsets, and we rank the features according to their predictive power, statistically demonstrating the top four to be spiculation, lobulation, subtlety, and calcification.

Usage Statistics

MedlinePlus

... this page: https://medlineplus.gov/usestatistics.html MedlinePlus Statistics To use the sharing features on this page, ... By Quarter View image full size Quarterly User Statistics Quarter Page Views Unique Visitors Oct-Dec-98 ...

Fundus autofluorescence in chronic essential hypertension.

PubMed

Ramezani, Alireza; Saberian, Peyman; Soheilian, Masoud; Parsa, Saeed Alipour; Kamali, Homayoun Koohi; Entezari, Morteza; Shahbazi, Mohammad-Mehdi; Yaseri, Mehdi

2014-01-01

To evaluate fundus autofluorescence (FAF) changes in patients with chronic essential hypertension (HTN). In this case-control study, 35 eyes of 35 patients with chronic essential HTN (lasting >5 years) and 31 eyes of 31 volunteers without history of HTN were included. FAF pictures were taken from right eyes of all cases with the Heidelberg retina angiography and then were assessed by two masked retinal specialists. In total, FAF images including 35 images of hypertensive patients and 31 pictures of volunteers, three apparently abnormal patterns were detected. A ring of hyper-autofluorescence in the central macula (doughnut-shaped) was observed in 9 (25.7%) eyes of the hypertensive group but only in 2 (6.5%) eyes of the control group. This difference was statistically significant (P = 0.036) between two groups. Hypo- and/or hyper-autofluorescence patches outside the fovea were the other sign found more in the hypertensive group (22.9%) than in the control group (6.5%); however, the difference was not statistically significant (P = 0.089). The third feature was hypo-autofluorescence around the disk noticed in 11 (31.4%) eyes of hypertensive patients compared to 8 (25.8%) eyes of the controls (P = 0.615). A ring of hyper-autofluorescence in the central macula forming a doughnut-shaped feature may be a FAF sign in patients with chronic essential HTN.

Multi-scale radiomic analysis of sub-cortical regions in MRI related to autism, gender and age

NASA Astrophysics Data System (ADS)

Chaddad, Ahmad; Desrosiers, Christian; Toews, Matthew

2017-03-01

We propose using multi-scale image textures to investigate links between neuroanatomical regions and clinical variables in MRI. Texture features are derived at multiple scales of resolution based on the Laplacian-of-Gaussian (LoG) filter. Three quantifier functions (Average, Standard Deviation and Entropy) are used to summarize texture statistics within standard, automatically segmented neuroanatomical regions. Significance tests are performed to identify regional texture differences between ASD vs. TDC and male vs. female groups, as well as correlations with age (corrected p < 0.05). The open-access brain imaging data exchange (ABIDE) brain MRI dataset is used to evaluate texture features derived from 31 brain regions from 1112 subjects including 573 typically developing control (TDC, 99 females, 474 males) and 539 Autism spectrum disorder (ASD, 65 female and 474 male) subjects. Statistically significant texture differences between ASD vs. TDC groups are identified asymmetrically in the right hippocampus, left choroid-plexus and corpus callosum (CC), and symmetrically in the cerebellar white matter. Sex-related texture differences in TDC subjects are found in primarily in the left amygdala, left cerebellar white matter, and brain stem. Correlations between age and texture in TDC subjects are found in the thalamus-proper, caudate and pallidum, most exhibiting bilateral symmetry.

Searching for the 3.5 keV Line in the Stacked Suzaku Observations of Galaxy Clusters

NASA Technical Reports Server (NTRS)

Bulbul, Esra; Markevitch, Maxim; Foster, Adam; Miller, Eric; Bautz, Mark; Lowenstein, Mike; Randall, Scott W.; Smith, Randall K.

2016-01-01

We perform a detailed study of the stacked Suzaku observations of 47 galaxy clusters, spanning a redshift range of 0.01-0.45, to search for the unidentified 3.5 keV line. This sample provides an independent test for the previously detected line. We detect a 2sigma-significant spectral feature at 3.5 keV in the spectrum of the full sample. When the sample is divided into two subsamples (cool-core and non-cool core clusters), the cool-core subsample shows no statistically significant positive residuals at the line energy. A very weak (approx. 2sigma confidence) spectral feature at 3.5 keV is permitted by the data from the non-cool-core clusters sample. The upper limit on a neutrino decay mixing angle of sin(sup 2)(2theta) = 6.1 x 10(exp -11) from the full Suzaku sample is consistent with the previous detections in the stacked XMM-Newton sample of galaxy clusters (which had a higher statistical sensitivity to faint lines), M31, and Galactic center, at a 90% confidence level. However, the constraint from the present sample, which does not include the Perseus cluster, is in tension with previously reported line flux observed in the core of the Perseus cluster with XMM-Newton and Suzaku.

Modeling Age-Friendly Environment, Active Aging, and Social Connectedness in an Emerging Asian Economy.

PubMed

Lai, Ming-Ming; Lein, Shi-Ying; Lau, Siok-Hwa; Lai, Ming-Ling

2016-01-01

This paper empirically tested eight key features of WHO guidelines to age-friendly community by surveying 211 informal caregivers and 402 self-care adults (aged 45 to 85 and above) in Malaysia. We examined the associations of these eight features with active aging and social connectedness through exploratory and confirmatory factor analyses. A structural model with satisfactory goodness-of-fit indices (CMIN/df = 1.11, RMSEA = 0.02, NFI = 0.97, TLI = 1.00, CFI = 1.00, and GFI = 0.96) indicates that transportation and housing, community support and health services, and outdoor spaces and buildings are statistically significant in creating an age-friendly environment. We found a statistically significant positive relationship between an age-friendly environment and active aging. This relationship is mediated by social connectedness. The results indicate that built environments such as accessible public transportations and housing, affordable and accessible healthcare services, and elderly friendly outdoor spaces and buildings have to be put into place before social environment in building an age-friendly environment. Otherwise, the structural barriers would hinder social interactions for the aged. The removal of the environmental barriers and improved public transportation services provide short-term solutions to meet the varied and growing needs of the older population.

Modeling Age-Friendly Environment, Active Aging, and Social Connectedness in an Emerging Asian Economy

PubMed Central

Lai, Ming-Ming; Lein, Shi-Ying; Lau, Siok-Hwa; Lai, Ming-Ling

2016-01-01

This paper empirically tested eight key features of WHO guidelines to age-friendly community by surveying 211 informal caregivers and 402 self-care adults (aged 45 to 85 and above) in Malaysia. We examined the associations of these eight features with active aging and social connectedness through exploratory and confirmatory factor analyses. A structural model with satisfactory goodness-of-fit indices (CMIN/df = 1.11, RMSEA = 0.02, NFI = 0.97, TLI = 1.00, CFI = 1.00, and GFI = 0.96) indicates that transportation and housing, community support and health services, and outdoor spaces and buildings are statistically significant in creating an age-friendly environment. We found a statistically significant positive relationship between an age-friendly environment and active aging. This relationship is mediated by social connectedness. The results indicate that built environments such as accessible public transportations and housing, affordable and accessible healthcare services, and elderly friendly outdoor spaces and buildings have to be put into place before social environment in building an age-friendly environment. Otherwise, the structural barriers would hinder social interactions for the aged. The removal of the environmental barriers and improved public transportation services provide short-term solutions to meet the varied and growing needs of the older population. PMID:27293889

Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images.

PubMed

Zhang, Lefei; Zhang, Qian; Du, Bo; Huang, Xin; Tang, Yuan Yan; Tao, Dacheng

2018-01-01

In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature, and morphological property, to improve the performances, e.g., the image classification accuracy. In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier. However, multiple features from various domains definitely have different physical meanings and statistical properties, and thus such concatenation has not efficiently explore the complementary properties among different features, which should benefit for boost the feature discriminability. Furthermore, it is also difficult to interpret the transformed results of the concatenated vector. Consequently, finding a physically meaningful consensus low dimensional feature representation of original multiple features is still a challenging task. In order to address these issues, we propose a novel feature learning framework, i.e., the simultaneous spectral-spatial feature selection and extraction algorithm, for hyperspectral images spectral-spatial feature representation and classification. Specifically, the proposed method learns a latent low dimensional subspace by projecting the spectral-spatial feature into a common feature space, where the complementary information has been effectively exploited, and simultaneously, only the most significant original features have been transformed. Encouraging experimental results on three public available hyperspectral remote sensing datasets confirm that our proposed method is effective and efficient.

Kepler Planet Detection Metrics: Statistical Bootstrap Test

NASA Technical Reports Server (NTRS)

Jenkins, Jon M.; Burke, Christopher J.

2016-01-01

This document describes the data produced by the Statistical Bootstrap Test over the final three Threshold Crossing Event (TCE) deliveries to NExScI: SOC 9.1 (Q1Q16)1 (Tenenbaum et al. 2014), SOC 9.2 (Q1Q17) aka DR242 (Seader et al. 2015), and SOC 9.3 (Q1Q17) aka DR253 (Twicken et al. 2016). The last few years have seen significant improvements in the SOC science data processing pipeline, leading to higher quality light curves and more sensitive transit searches. The statistical bootstrap analysis results presented here and the numerical results archived at NASAs Exoplanet Science Institute (NExScI) bear witness to these software improvements. This document attempts to introduce and describe the main features and differences between these three data sets as a consequence of the software changes.

Landsat analysis for uranium exploration in Northeast Turkey

USGS Publications Warehouse

Lee, Keenan

1983-01-01

No uranium deposits are known in the Trabzon, Turkey region, and consequently, exploration criteria have not been defined. Nonetheless, by analogy with uranium deposits studied elsewhere, exploration guides are suggested to include dense concentrations of linear features, lineaments -- especially with northwest trend, acidic plutonic rocks, and alteration indicated by limonite. A suite of digitally processed images of a single Landsat scene served as the image base for mapping 3,376 linear features. Analysis of the linear feature data yielded two statistically significant trends, which in turn defined two sets of strong lineaments. Color composite images were used to map acidic plutonic rocks and areas of surficial limonitic materials. The Landsat interpretation yielded a map of these exploration guides that may be used to evaluate relative uranium potential. One area in particular shows a high coincidence of favorable indicators.

A comparative study of hematological parameters of α and β thalassemias in a high prevalence zone: Saudi Arabia.

PubMed

Mehdi, Syed Riaz; Al Dahmash, Badr Abdullah

2011-09-01

Saudi Arabia falls in the high prevalent zone of αα and β thalassemias. Early screening for the type of thalassemia is essential for further investigations and management. The study was carried out to differentiate the type of thalassemia based on red cell indices and other hematological parameters. The study was carried out on 991 clinically suspected cases of thalassemias in Riyadh, Saudi Arabia. The hematological parameters were studied on Coulter STKS. Cellulose acetate hemoglobin electrophoresis and high-performance liquid chromatography (HPLC) were performed on all the blood samples. Gene deletion studies were carried out by restriction fragment length polymorphism (RFLP) technique using the restriction endonucleases Bam HI. Statistical analysis was performed on SPSS 11.5 version. The hemoglobin electrophoresis and gene studies revealed that there were 406 (40.96%) and 59 (5.95 %) cases of β thalassemia trait and β thalassemia major respectively including adults and children. 426 cases of various deletion forms of α thalassemias were seen. Microcytosis was a common feature in β thalassemias trait and (-α/-α) and (--/αα) types of α thalassemias. MCH was a more significant distinguishing feature among thalassemias. β thalassemia major and α thalassemia (-α/αα) had almost normal hematological parameters. MCV and RBC counts are not statistically significant features for discriminating between α and β thalassemias. There is need for development of a discrimination index to differentiate between α and β thalassemias traits on the lines of discriminatory Indices available for distinguishing β thalassemias trait from iron deficiency anemia.

Contrasting effects of feature-based statistics on the categorisation and basic-level identification of visual objects.

PubMed

Taylor, Kirsten I; Devereux, Barry J; Acres, Kadia; Randall, Billi; Tyler, Lorraine K

2012-03-01

Conceptual representations are at the heart of our mental lives, involved in every aspect of cognitive functioning. Despite their centrality, a long-standing debate persists as to how the meanings of concepts are represented and processed. Many accounts agree that the meanings of concrete concepts are represented by their individual features, but disagree about the importance of different feature-based variables: some views stress the importance of the information carried by distinctive features in conceptual processing, others the features which are shared over many concepts, and still others the extent to which features co-occur. We suggest that previously disparate theoretical positions and experimental findings can be unified by an account which claims that task demands determine how concepts are processed in addition to the effects of feature distinctiveness and co-occurrence. We tested these predictions in a basic-level naming task which relies on distinctive feature information (Experiment 1) and a domain decision task which relies on shared feature information (Experiment 2). Both used large-scale regression designs with the same visual objects, and mixed-effects models incorporating participant, session, stimulus-related and feature statistic variables to model the performance. We found that concepts with relatively more distinctive and more highly correlated distinctive relative to shared features facilitated basic-level naming latencies, while concepts with relatively more shared and more highly correlated shared relative to distinctive features speeded domain decisions. These findings demonstrate that the feature statistics of distinctiveness (shared vs. distinctive) and correlational strength, as well as the task demands, determine how concept meaning is processed in the conceptual system. Copyright © 2011 Elsevier B.V. All rights reserved.

Single Particle Differentiation through 2D Optical Fiber Trapping and Back-Scattered Signal Statistical Analysis: An Exploratory Approach

PubMed Central

Ribeiro, Rita S. R.; Cunha, João P. S.; Rosa, Carla C.; Jorge, Pedro A. S.

2018-01-01

Recent trends on microbiology point out the urge to develop optical micro-tools with multifunctionalities such as simultaneous manipulation and sensing. Considering that miniaturization has been recognized as one of the most important paradigms of emerging sensing biotechnologies, optical fiber tools, including Optical Fiber Tweezers (OFTs), are suitable candidates for developing multifunctional small sensors for Medicine and Biology. OFTs are flexible and versatile optotools based on fibers with one extremity patterned to form a micro-lens. These are able to focus laser beams and exert forces onto microparticles strong enough (piconewtons) to trap and manipulate them. In this paper, through an exploratory analysis of a 45 features set, including time and frequency-domain parameters of the back-scattered signal of particles trapped by a polymeric lens, we created a novel single feature able to differentiate synthetic particles (PMMA and Polystyrene) from living yeasts cells. This single statistical feature can be useful for the development of label-free hybrid optical fiber sensors with applications in infectious diseases detection or cells sorting. It can also contribute, by revealing the most significant information that can be extracted from the scattered signal, to the development of a simpler method for particles characterization (in terms of composition, heterogeneity degree) than existent technologies. PMID:29495502

A Realistic Seizure Prediction Study Based on Multiclass SVM.

PubMed

Direito, Bruno; Teixeira, César A; Sales, Francisco; Castelo-Branco, Miguel; Dourado, António

2017-05-01

A patient-specific algorithm, for epileptic seizure prediction, based on multiclass support-vector machines (SVM) and using multi-channel high-dimensional feature sets, is presented. The feature sets, combined with multiclass classification and post-processing schemes aim at the generation of alarms and reduced influence of false positives. This study considers 216 patients from the European Epilepsy Database, and includes 185 patients with scalp EEG recordings and 31 with intracranial data. The strategy was tested over a total of 16,729.80[Formula: see text]h of inter-ictal data, including 1206 seizures. We found an overall sensitivity of 38.47% and a false positive rate per hour of 0.20. The performance of the method achieved statistical significance in 24 patients (11% of the patients). Despite the encouraging results previously reported in specific datasets, the prospective demonstration on long-term EEG recording has been limited. Our study presents a prospective analysis of a large heterogeneous, multicentric dataset. The statistical framework based on conservative assumptions, reflects a realistic approach compared to constrained datasets, and/or in-sample evaluations. The improvement of these results, with the definition of an appropriate set of features able to improve the distinction between the pre-ictal and nonpre-ictal states, hence minimizing the effect of confounding variables, remains a key aspect.

Photoanthropometric study of dysmorphic features of the face in children with autism and asperger syndrome.

PubMed

Gorczyca, Piotr; Kapinos-Gorczyca, Agnieszka; Ziora, Katarzyna; Oświęcimska, Joanna

2012-01-01

Childhood autism is a neurodevelopmental disorder characterized by impairments in social interactions, verbal and non-verbal communication and by a pattern of stereotypical behaviors and interests. The aim of this study was to estimate the dysmorphic facial features of children with autism and children with Asperger syndrome. The examination was conducted on 60 children (30 with childhood autism and 30 with Asperger syndrome). The photo anthropometric method used in this study followed the protocol established by Stengel-Rutkowski et al. The performed statistical analysis showed that in patients with childhood autism, the anteriorly rotated ears and the long back of the nose appeared more often. In the group of children with autism, there was a connection between the amount of dysmorphies and the presence of some somatic diseases in the first-degree relatives. There was also a connection between the motor coordination and the age the child began to walk. In patients with childhood autism, there were certain dysmorphies (like the anterior rotated ears and the long back of the nose) which appeared more often. Although the connection was not statistically significant, it seemed to concur with data from the literature. Formulation of the other conclusions would require broader studies e.g. dealing with a familial analysis of dysmorphic features.

A New Method for Automated Identification and Morphometry of Myelinated Fibers Through Light Microscopy Image Analysis.

PubMed

Novas, Romulo Bourget; Fazan, Valeria Paula Sassoli; Felipe, Joaquim Cezar

2016-02-01

Nerve morphometry is known to produce relevant information for the evaluation of several phenomena, such as nerve repair, regeneration, implant, transplant, aging, and different human neuropathies. Manual morphometry is laborious, tedious, time consuming, and subject to many sources of error. Therefore, in this paper, we propose a new method for the automated morphometry of myelinated fibers in cross-section light microscopy images. Images from the recurrent laryngeal nerve of adult rats and the vestibulocochlear nerve of adult guinea pigs were used herein. The proposed pipeline for fiber segmentation is based on the techniques of competitive clustering and concavity analysis. The evaluation of the proposed method for segmentation of images was done by comparing the automatic segmentation with the manual segmentation. To further evaluate the proposed method considering morphometric features extracted from the segmented images, the distributions of these features were tested for statistical significant difference. The method achieved a high overall sensitivity and very low false-positive rates per image. We detect no statistical difference between the distribution of the features extracted from the manual and the pipeline segmentations. The method presented a good overall performance, showing widespread potential in experimental and clinical settings allowing large-scale image analysis and, thus, leading to more reliable results.

Single Particle Differentiation through 2D Optical Fiber Trapping and Back-Scattered Signal Statistical Analysis: An Exploratory Approach.

PubMed

Paiva, Joana S; Ribeiro, Rita S R; Cunha, João P S; Rosa, Carla C; Jorge, Pedro A S

2018-02-27

Recent trends on microbiology point out the urge to develop optical micro-tools with multifunctionalities such as simultaneous manipulation and sensing. Considering that miniaturization has been recognized as one of the most important paradigms of emerging sensing biotechnologies, optical fiber tools, including Optical Fiber Tweezers (OFTs), are suitable candidates for developing multifunctional small sensors for Medicine and Biology. OFTs are flexible and versatile optotools based on fibers with one extremity patterned to form a micro-lens. These are able to focus laser beams and exert forces onto microparticles strong enough (piconewtons) to trap and manipulate them. In this paper, through an exploratory analysis of a 45 features set, including time and frequency-domain parameters of the back-scattered signal of particles trapped by a polymeric lens, we created a novel single feature able to differentiate synthetic particles (PMMA and Polystyrene) from living yeasts cells. This single statistical feature can be useful for the development of label-free hybrid optical fiber sensors with applications in infectious diseases detection or cells sorting. It can also contribute, by revealing the most significant information that can be extracted from the scattered signal, to the development of a simpler method for particles characterization (in terms of composition, heterogeneity degree) than existent technologies.

School Violence: Data & Statistics

MedlinePlus

... Data LGB Youth Report School Violence Featured Topic: Bullying Research Featured Topic: Prevent Gang Membership Featured Topic: ... report covers topics such as victimization, teacher injury, bullying, school conditions, fights, weapons, and student use of ...

Features versus context: An approach for precise and detailed detection and delineation of faces and facial features.

PubMed

Ding, Liya; Martinez, Aleix M

2010-11-01

The appearance-based approach to face detection has seen great advances in the last several years. In this approach, we learn the image statistics describing the texture pattern (appearance) of the object class we want to detect, e.g., the face. However, this approach has had limited success in providing an accurate and detailed description of the internal facial features, i.e., eyes, brows, nose, and mouth. In general, this is due to the limited information carried by the learned statistical model. While the face template is relatively rich in texture, facial features (e.g., eyes, nose, and mouth) do not carry enough discriminative information to tell them apart from all possible background images. We resolve this problem by adding the context information of each facial feature in the design of the statistical model. In the proposed approach, the context information defines the image statistics most correlated with the surroundings of each facial component. This means that when we search for a face or facial feature, we look for those locations which most resemble the feature yet are most dissimilar to its context. This dissimilarity with the context features forces the detector to gravitate toward an accurate estimate of the position of the facial feature. Learning to discriminate between feature and context templates is difficult, however, because the context and the texture of the facial features vary widely under changing expression, pose, and illumination, and may even resemble one another. We address this problem with the use of subclass divisions. We derive two algorithms to automatically divide the training samples of each facial feature into a set of subclasses, each representing a distinct construction of the same facial component (e.g., closed versus open eyes) or its context (e.g., different hairstyles). The first algorithm is based on a discriminant analysis formulation. The second algorithm is an extension of the AdaBoost approach. We provide extensive experimental results using still images and video sequences for a total of 3,930 images. We show that the results are almost as good as those obtained with manual detection.

PROMISE: a tool to identify genomic features with a specific biologically interesting pattern of associations with multiple endpoint variables

PubMed Central

Pounds, Stan; Cheng, Cheng; Cao, Xueyuan; Crews, Kristine R.; Plunkett, William; Gandhi, Varsha; Rubnitz, Jeffrey; Ribeiro, Raul C.; Downing, James R.; Lamba, Jatinder

2009-01-01

Motivation: In some applications, prior biological knowledge can be used to define a specific pattern of association of multiple endpoint variables with a genomic variable that is biologically most interesting. However, to our knowledge, there is no statistical procedure designed to detect specific patterns of association with multiple endpoint variables. Results: Projection onto the most interesting statistical evidence (PROMISE) is proposed as a general procedure to identify genomic variables that exhibit a specific biologically interesting pattern of association with multiple endpoint variables. Biological knowledge of the endpoint variables is used to define a vector that represents the biologically most interesting values for statistics that characterize the associations of the endpoint variables with a genomic variable. A test statistic is defined as the dot-product of the vector of the observed association statistics and the vector of the most interesting values of the association statistics. By definition, this test statistic is proportional to the length of the projection of the observed vector of correlations onto the vector of most interesting associations. Statistical significance is determined via permutation. In simulation studies and an example application, PROMISE shows greater statistical power to identify genes with the interesting pattern of associations than classical multivariate procedures, individual endpoint analyses or listing genes that have the pattern of interest and are significant in more than one individual endpoint analysis. Availability: Documented R routines are freely available from www.stjuderesearch.org/depts/biostats and will soon be available as a Bioconductor package from www.bioconductor.org. Contact: stanley.pounds@stjude.org Supplementary information: Supplementary data are available at Bioinformatics online. PMID:19528086

The Research of Feature Extraction Method of Liver Pathological Image Based on Multispatial Mapping and Statistical Properties

PubMed Central

Liu, Huiling; Xia, Bingbing; Yi, Dehui

2016-01-01

We propose a new feature extraction method of liver pathological image based on multispatial mapping and statistical properties. For liver pathological images of Hematein Eosin staining, the image of R and B channels can reflect the sensitivity of liver pathological images better, while the entropy space and Local Binary Pattern (LBP) space can reflect the texture features of the image better. To obtain the more comprehensive information, we map liver pathological images to the entropy space, LBP space, R space, and B space. The traditional Higher Order Local Autocorrelation Coefficients (HLAC) cannot reflect the overall information of the image, so we propose an average correction HLAC feature. We calculate the statistical properties and the average gray value of pathological images and then update the current pixel value as the absolute value of the difference between the current pixel gray value and the average gray value, which can be more sensitive to the gray value changes of pathological images. Lastly the HLAC template is used to calculate the features of the updated image. The experiment results show that the improved features of the multispatial mapping have the better classification performance for the liver cancer. PMID:27022407

Quality evaluation of no-reference MR images using multidirectional filters and image statistics.

PubMed

Jang, Jinseong; Bang, Kihun; Jang, Hanbyol; Hwang, Dosik

2018-09-01

This study aimed to develop a fully automatic, no-reference image-quality assessment (IQA) method for MR images. New quality-aware features were obtained by applying multidirectional filters to MR images and examining the feature statistics. A histogram of these features was then fitted to a generalized Gaussian distribution function for which the shape parameters yielded different values depending on the type of distortion in the MR image. Standard feature statistics were established through a training process based on high-quality MR images without distortion. Subsequently, the feature statistics of a test MR image were calculated and compared with the standards. The quality score was calculated as the difference between the shape parameters of the test image and the undistorted standard images. The proposed IQA method showed a >0.99 correlation with the conventional full-reference assessment methods; accordingly, this proposed method yielded the best performance among no-reference IQA methods for images containing six types of synthetic, MR-specific distortions. In addition, for authentically distorted images, the proposed method yielded the highest correlation with subjective assessments by human observers, thus demonstrating its superior performance over other no-reference IQAs. Our proposed IQA was designed to consider MR-specific features and outperformed other no-reference IQAs designed mainly for photographic images. Magn Reson Med 80:914-924, 2018. © 2018 International Society for Magnetic Resonance in Medicine. © 2018 International Society for Magnetic Resonance in Medicine.

Personality Traits and Decision on Breast Reconstruction in Women after Mastectomy.

PubMed

Miśkiewicz, Halina; Antoszewski, Bogusław; Iljin, Aleksandra

2016-09-01

The aim of the study was evaluation of the correlation between selected personality traits in women after mastectomy and their decision on breast reconstruction. The study was conducted between 2013‑2015, in the Department of Plastic, Reconstructive and Aesthetic Surgery, Medical University of Lodz, and Department of Oncological and Breast Surgery, CZMP. Comparative analysis comprised 40 patients, in whom mastectomy and breast reconstruction was done, and 40 women after breast amputation, who did not undergo reconstructive surgery. Basing on self-constructed questionnaire, five features of personality were evaluated in these women: pursue of success in life, ability to motivate others, openness to other people, impact of belonging to a social group on sense of security and the importance of opinion of others about the respondent. Apart from the questionnaire, in both groups of women a psychologic tool was used (SUPIN S30 and C30 tests) to determine the intensity of positive and negative emotions. Women who did not choose the reconstructive option were statistically significantly older at mastectomy than women who underwent breast reconstruction. There were statistically significant differences between both groups in response to question on being open to other people and value of other people's opinion. The differences in responses to question on the impact of belonging to a social group on personal sense of safety were hardly statistically significant. In psychometric studies there were significant differences in responses to SUPIN C30 test for negative emotions and S-30 for positive emotions. The level of negative emotions - feature of group A was in 47.5% in the range of high scores and in 47.5% within low and low-average scores. Among women from group B 57.5% had high scores, while 37.5% low and average scores. There were significant differences in the results of positive emotions evaluation in S-30. Women who did not undergo breast reconstruction usually had high scores, while those who decided on reconstructive surgery usually had low scores and low-high scores. 1. The decision on breast reconstruction after mastectomy is connected with personality features of patients. Introvert women, who base their self-opinion on opinion of others and their sense of security on belonging to a social group, rarely choose to undergo breast reconstruction. 2. Younger patients after mastectomy more frequently choose the breast reconstructive option. 3. A special algorithm of medical and psychological care in patients after mastectomy should be created to improve their further quality of life.

[Step Fisher discriminant analysis on severe clinical features of hand foot and mouth disease between enterovirus (EV) 71 and other EV].

PubMed

Ruan, Feng; Tan, Ai-jun; Zhang, Xue-bao; Chen, Xue-qin; Xiao, Song-jian; Ye, Zhong-wen; Wang, Song

2011-07-01

To compare the clinical features of severe hand foot and mouth disease between enterovirus (EV) 71 and other EV to find specific diagnosis index of EV71 severe hand foot and mouth disease. Case definition were adopted from national guideline of hand foot and mouth disease diagnose (Version 2010). Clinical data of severe hand foot and mouth disease came from case history and contents of questionnaire would include the ones between the time of onset and diagnoses being made. EV and EV71, Cox A16 nucleic acid tested were by RT-PCR in stool samples. Clinical features of severe hand foot and mouth disease between EV71 and other EV were compare. There appeared statistical differences between neurologic symptoms such as tremor, myoclonic jerk, listlessness, convulsion and white blood cell counts in CSF (P < 0.05). Results from the step Fisher discriminant analysis showed only tremor and white blood cell had an increase in CSF, with statistically significant differences. The discriminant equation of EV71 was Y = 3.059X(1) + 3.83X(5) - 2.742 and the equation of other EV was Y = 1.634X(1) + 1.623X(5) - 1.693. The specificity of EV71 was 91% and the specificity of other EV was 40%. The increase of clinical features of tremor and white blood cell in CSF could be used as diagnosis index of severe EV71.

Mounting ground sections of teeth: Cyanoacrylate adhesive versus Canada balsam.

PubMed

Vangala, Manogna Rl; Rudraraju, Amrutha; Subramanyam, R V

2016-01-01

Hard tissues can be studied by either decalcification or by preparing ground sections. Various mounting media have been tried and used for ground sections of teeth. However, there are very few studies on the use of cyanoacrylate adhesive as a mounting medium. The aim of our study was to evaluate the efficacy of cyanoacrylate adhesive (Fevikwik™) as a mounting medium for ground sections of teeth and to compare these ground sections with those mounted with Canada balsam. Ground sections were prepared from twenty extracted teeth. Each section was divided into two halves and mounted on one slide, one with cyanoacrylate adhesive (Fevikwik™) and the other with Canada balsam. Scoring for various features in the ground sections was done by two independent observers. Statistical analysis using Student's t-test (unpaired) of average scores was performed for each feature observed. No statistically significant difference was found between the two for most of the features. However, cyanoacrylate was found to be better than Canada balsam for observing striae of Retzius (P < 0.0205), enamel lamellae (P < 0.036), dentinal tubules (P < 0.0057), interglobular dentin (P < 0.0001), sclerotic dentin - transmitted light (P < 0.00001), sclerotic dentin - polarized light (P < 0.0002) and Sharpey's fibers (P < 0.0004). This initial study shows that cyanoacrylate is better than Canada balsam for observing certain features of ground sections of teeth. However, it remains to be seen whether it will be useful for studying undecalcified sections of carious teeth and for soft tissue sections.

Quantifying memory in complex physiological time-series.

PubMed

Shirazi, Amir H; Raoufy, Mohammad R; Ebadi, Haleh; De Rui, Michele; Schiff, Sami; Mazloom, Roham; Hajizadeh, Sohrab; Gharibzadeh, Shahriar; Dehpour, Ahmad R; Amodio, Piero; Jafari, G Reza; Montagnese, Sara; Mani, Ali R

2013-01-01

In a time-series, memory is a statistical feature that lasts for a period of time and distinguishes the time-series from a random, or memory-less, process. In the present study, the concept of "memory length" was used to define the time period, or scale over which rare events within a physiological time-series do not appear randomly. The method is based on inverse statistical analysis and provides empiric evidence that rare fluctuations in cardio-respiratory time-series are 'forgotten' quickly in healthy subjects while the memory for such events is significantly prolonged in pathological conditions such as asthma (respiratory time-series) and liver cirrhosis (heart-beat time-series). The memory length was significantly higher in patients with uncontrolled asthma compared to healthy volunteers. Likewise, it was significantly higher in patients with decompensated cirrhosis compared to those with compensated cirrhosis and healthy volunteers. We also observed that the cardio-respiratory system has simple low order dynamics and short memory around its average, and high order dynamics around rare fluctuations.

Quantifying Memory in Complex Physiological Time-Series

PubMed Central

Shirazi, Amir H.; Raoufy, Mohammad R.; Ebadi, Haleh; De Rui, Michele; Schiff, Sami; Mazloom, Roham; Hajizadeh, Sohrab; Gharibzadeh, Shahriar; Dehpour, Ahmad R.; Amodio, Piero; Jafari, G. Reza; Montagnese, Sara; Mani, Ali R.

2013-01-01

In a time-series, memory is a statistical feature that lasts for a period of time and distinguishes the time-series from a random, or memory-less, process. In the present study, the concept of “memory length” was used to define the time period, or scale over which rare events within a physiological time-series do not appear randomly. The method is based on inverse statistical analysis and provides empiric evidence that rare fluctuations in cardio-respiratory time-series are ‘forgotten’ quickly in healthy subjects while the memory for such events is significantly prolonged in pathological conditions such as asthma (respiratory time-series) and liver cirrhosis (heart-beat time-series). The memory length was significantly higher in patients with uncontrolled asthma compared to healthy volunteers. Likewise, it was significantly higher in patients with decompensated cirrhosis compared to those with compensated cirrhosis and healthy volunteers. We also observed that the cardio-respiratory system has simple low order dynamics and short memory around its average, and high order dynamics around rare fluctuations. PMID:24039811

Dissociative features in posttraumatic stress disorder: A latent profile analysis.

PubMed

Műllerová, Jana; Hansen, Maj; Contractor, Ateka A; Elhai, Jon D; Armour, Cherie

2016-09-01

The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) characterizes the dissociative subtype of posttraumatic stress disorder (PTSD) in terms of the individual meeting the criteria for PTSD and additionally reporting symptoms of depersonalization and/or derealization. The current study aimed to examine whether a dissociative PTSD profile may include alternative features of dissociation and whether it could be differentiated from a nondissociative PTSD profile on certain psychopathologies and demographics. Data from 309 trauma-exposed participants, collected through Amazon Mechanical Turk, were subjected to latent profile analysis. Regression analyses were used to examine the predictors of latent classes. Three discrete profiles named Baseline, PTSD, and Dissociative profile were uncovered. All examined features of dissociation were significantly elevated in the Dissociative profile. Anxiety, male sex, being employed, and having a minority racial background significantly predicted the Dissociative profile relative to the PTSD profile. The study points to the importance of alternative symptoms of dissociation in the dissociative PTSD subtype beyond the symptoms of depersonalization and derealization. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

[Features of allele polymorphism of genes involved in homocysteine and folate metabolism in patients with atherosclerosis of the lower extremity arteries].

PubMed

Klenkova, N A; Kapustin, S I; Saltykova, N B; Shmeleva, V M; Blinov, M N

2009-01-01

Under study were features of allele polymorphism of genes of methylenetetrahydrofolate reductase (MTHFR C677T and A1298C), methionine synthase (MS A 2756G), methionine synthase reductase (MTRR A66G) and methylenetetrahydrofolate dehydrogenase (MTHFD G1958A) in patients with atherosclerosis of the lower extremity arteries (ALEA). Patients with hyperhomocysteinemia (HHcy) had statistically significant increase of allele MTHFR 677T and MTRR 66GG as compared both with the control group and with the group of patients without HHcy. It suggests that polymorphism of genes involved in homocystein and folate metabolism might affect the risk of HHcy in patients with ALEA.

The Effects of Legumes on Metabolic Features, Insulin Resistance and Hepatic Function Tests in Women with Central Obesity: A Randomized Controlled Trial

PubMed Central

Alizadeh, Mohammad; Gharaaghaji, Rasool; Gargari, Bahram Pourghassem

2014-01-01

Background: The effect of high-legume hypocaloric diet on metabolic features in women is unclear. This study provided an opportunity to find effects of high-legume diet on metabolic features in women who consumed high legumes at pre-study period. Methods: In this randomized controlled trial after 2 weeks of a run-in period on an isocaloric diet, 42 premenopausal women with central obesity were randomly assigned into two groups: (1) Hypocaloric diet enriched in legumes (HDEL) and (2) hypocaloric diet without legumes (HDWL) for 6 weeks. The following variables were assessed before intervention and 3 and 6 weeks after its beginning: Waist circumference (WC), systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting serum concentrations of triglyceride (TG), high density lipoprotein cholesterol, fasting blood sugar (FBS), insulin, homeostasis model of insulin resistance (HOMA-IR), alanine aminotransferase (ALT) and aspartate aminotransferase (AST). We used multifactor model of nested multivariate analysis of variance repeated measurements and t-test for statistical analysis. Results: HDEL and HDWL significantly reduced the WC. HDEL significantly reduced the SBP and TG. Both HDEL and HDWL significantly increased fasting concentration of insulin and HOMA-IR after 3 weeks, but their significant effects on insulin disappeared after 6 weeks and HDEL returned HOMA-IR to basal levels in the subsequent 3 weeks. In HDEL group percent of decrease in AST and ALT between 3rd and 6th weeks was significant. In HDWL group percent of increase in SBP, DBP, FBS and TG between 3rd and 6th weeks was significant. Conclusions: The study indicated beneficial effects of hypocaloric legumes on metabolic features. PMID:25013690

Randomized clinical trials in implant therapy: relationships among methodological, statistical, clinical, paratextual features and number of citations.

PubMed

Nieri, Michele; Clauser, Carlo; Franceschi, Debora; Pagliaro, Umberto; Saletta, Daniele; Pini-Prato, Giovanpaolo

2007-08-01

The aim of the present study was to investigate the relationships among reported methodological, statistical, clinical and paratextual variables of randomized clinical trials (RCTs) in implant therapy, and their influence on subsequent research. The material consisted of the RCTs in implant therapy published through the end of the year 2000. Methodological, statistical, clinical and paratextual features of the articles were assessed and recorded. The perceived clinical relevance was subjectively evaluated by an experienced clinician on anonymous abstracts. The impact on research was measured by the number of citations found in the Science Citation Index. A new statistical technique (Structural learning of Bayesian Networks) was used to assess the relationships among the considered variables. Descriptive statistics revealed that the reported methodology and statistics of RCTs in implant therapy were defective. Follow-up of the studies was generally short. The perceived clinical relevance appeared to be associated with the objectives of the studies and with the number of published images in the original articles. The impact on research was related to the nationality of the involved institutions and to the number of published images. RCTs in implant therapy (until 2000) show important methodological and statistical flaws and may not be appropriate for guiding clinicians in their practice. The methodological and statistical quality of the studies did not appear to affect their impact on practice and research. Bayesian Networks suggest new and unexpected relationships among the methodological, statistical, clinical and paratextual features of RCTs.

An adaptive multi-feature segmentation model for infrared image

NASA Astrophysics Data System (ADS)

Zhang, Tingting; Han, Jin; Zhang, Yi; Bai, Lianfa

2016-04-01

Active contour models (ACM) have been extensively applied to image segmentation, conventional region-based active contour models only utilize global or local single feature information to minimize the energy functional to drive the contour evolution. Considering the limitations of original ACMs, an adaptive multi-feature segmentation model is proposed to handle infrared images with blurred boundaries and low contrast. In the proposed model, several essential local statistic features are introduced to construct a multi-feature signed pressure function (MFSPF). In addition, we draw upon the adaptive weight coefficient to modify the level set formulation, which is formed by integrating MFSPF with local statistic features and signed pressure function with global information. Experimental results demonstrate that the proposed method can make up for the inadequacy of the original method and get desirable results in segmenting infrared images.

Vehicle license plate recognition based on geometry restraints and multi-feature decision

NASA Astrophysics Data System (ADS)

Wu, Jianwei; Wang, Zongyue

2005-10-01

Vehicle license plate (VLP) recognition is of great importance to many traffic applications. Though researchers have paid much attention to VLP recognition there has not been a fully operational VLP recognition system yet for many reasons. This paper discusses a valid and practical method for vehicle license plate recognition based on geometry restraints and multi-feature decision including statistical and structural features. In general, the VLP recognition includes the following steps: the location of VLP, character segmentation, and character recognition. This paper discusses the three steps in detail. The characters of VLP are always declining caused by many factors, which makes it more difficult to recognize the characters of VLP, therefore geometry restraints such as the general ratio of length and width, the adjacent edges being perpendicular are used for incline correction. Image Moment has been proved to be invariant to translation, rotation and scaling therefore image moment is used as one feature for character recognition. Stroke is the basic element for writing and hence taking it as a feature is helpful to character recognition. Finally we take the image moment, the strokes and the numbers of each stroke for each character image and some other structural features and statistical features as the multi-feature to match each character image with sample character images so that each character image can be recognized by BP neural net. The proposed method combines statistical and structural features for VLP recognition, and the result shows its validity and efficiency.

Differentiation of women with premenstrual dysphoric disorder, recurrent brief depression, and healthy controls by daily mood rating dynamics.

PubMed

Pincus, Steven M; Schmidt, Peter J; Palladino-Negro, Paula; Rubinow, David R

2008-04-01

Enhanced statistical characterization of mood-rating data holds the potential to more precisely classify and sub-classify recurrent mood disorders like premenstrual dysphoric disorder (PMDD) and recurrent brief depressive disorder (RBD). We applied several complementary statistical methods to differentiate mood rating dynamics among women with PMDD, RBD, and normal controls (NC). We compared three subgroups of women: NC (n=8); PMDD (n=15); and RBD (n=9) on the basis of daily self-ratings of sadness, study lengths between 50 and 120 days. We analyzed mean levels; overall variability, SD; sequential irregularity, approximate entropy (ApEn); and a quantification of the extent of brief and staccato dynamics, denoted 'Spikiness'. For each of SD, irregularity (ApEn), and Spikiness, we showed highly significant subgroup differences, ANOVA0.001 for each statistic; additionally, many paired subgroup comparisons showed highly significant differences. In contrast, mean levels were indistinct among the subgroups. For SD, normal controls had much smaller levels than the other subgroups, with RBD intermediate. ApEn showed PMDD to be significantly more regular than the other subgroups. Spikiness showed NC and RBD data sets to be much more staccato than their PMDD counterparts, and appears to suitably characterize the defining feature of RBD dynamics. Compound criteria based on these statistical measures discriminated diagnostic subgroups with high sensitivity and specificity. Taken together, the statistical suite provides well-defined specifications of each subgroup. This can facilitate accurate diagnosis, and augment the prediction and evaluation of response to treatment. The statistical methodologies have broad and direct applicability to behavioral studies for many psychiatric disorders, and indeed to similar analyses of associated biological signals across multiple axes.

RAId_DbS: Peptide Identification using Database Searches with Realistic Statistics

PubMed Central

Alves, Gelio; Ogurtsov, Aleksey Y; Yu, Yi-Kuo

2007-01-01

Background The key to mass-spectrometry-based proteomics is peptide identification. A major challenge in peptide identification is to obtain realistic E-values when assigning statistical significance to candidate peptides. Results Using a simple scoring scheme, we propose a database search method with theoretically characterized statistics. Taking into account possible skewness in the random variable distribution and the effect of finite sampling, we provide a theoretical derivation for the tail of the score distribution. For every experimental spectrum examined, we collect the scores of peptides in the database, and find good agreement between the collected score statistics and our theoretical distribution. Using Student's t-tests, we quantify the degree of agreement between the theoretical distribution and the score statistics collected. The T-tests may be used to measure the reliability of reported statistics. When combined with reported P-value for a peptide hit using a score distribution model, this new measure prevents exaggerated statistics. Another feature of RAId_DbS is its capability of detecting multiple co-eluted peptides. The peptide identification performance and statistical accuracy of RAId_DbS are assessed and compared with several other search tools. The executables and data related to RAId_DbS are freely available upon request. PMID:17961253

Combining Statistical and Geometric Features for Colonic Polyp Detection in CTC Based on Multiple Kernel Learning

PubMed Central

Wang, Shijun; Yao, Jianhua; Petrick, Nicholas; Summers, Ronald M.

2010-01-01

Colon cancer is the second leading cause of cancer-related deaths in the United States. Computed tomographic colonography (CTC) combined with a computer aided detection system provides a feasible approach for improving colonic polyps detection and increasing the use of CTC for colon cancer screening. To distinguish true polyps from false positives, various features extracted from polyp candidates have been proposed. Most of these traditional features try to capture the shape information of polyp candidates or neighborhood knowledge about the surrounding structures (fold, colon wall, etc.). In this paper, we propose a new set of shape descriptors for polyp candidates based on statistical curvature information. These features called histograms of curvature features are rotation, translation and scale invariant and can be treated as complementing existing feature set. Then in order to make full use of the traditional geometric features (defined as group A) and the new statistical features (group B) which are highly heterogeneous, we employed a multiple kernel learning method based on semi-definite programming to learn an optimized classification kernel from the two groups of features. We conducted leave-one-patient-out test on a CTC dataset which contained scans from 66 patients. Experimental results show that a support vector machine (SVM) based on the combined feature set and the semi-definite optimization kernel achieved higher FROC performance compared to SVMs using the two groups of features separately. At a false positive per scan rate of 5, the sensitivity of the SVM using the combined features improved from 0.77 (Group A) and 0.73 (Group B) to 0.83 (p ≤ 0.01). PMID:20953299

[Features of metabolic syndrome in patients with depressive disorder].

PubMed

Zeman, M; Jirák, R; Zák, A; Jáchymová, M; Vecka, M; Tvrzická, E; Vávrová, L; Kodydková, J; Stanková, B

2009-01-01

Depressive disorder is a serious illness with a high incidence, proxime accessit after anxiety disorders among the psychiatric diseases. It is accompanied by an increased risk of development of type 2 diabetes mellitus, cardiovascular disease, and by increased all-cause mortality. Recently published data have suggested that factors connected with the insulin resistance are at the background of this association. In this pilot study we have investigated parameters of lipid metabolism and glucose homeostasis in consecutively admitted patients suffering from depressive disorder (DD) (group of 42 people), in 57 patients with the metabolic syndrome (MetS) and in a control group of 49 apparently healthy persons (CON). Depressive patients did not differ from the control group by age or body mass index (BMI) value, but they had statistically significantly higher concentrations of serum insulin, C-peptide, glucose, triglycerides (TG), conjugated dienes in LDL particles (CD-LDL), higher value of microalbuminuria and of insulin resistance (HOMA-IR) index. They simultaneously had significantly lower value of the insulin sensitivity (QUICKI) index. In comparison with the MetS group the depressive patients were characterized by significantly lower both systolic and diastolic blood pressure, BMI , serum TG, apolipoprotein B, uric acid, C-peptide and by higher concentrations of apolipoprotein A-I and HDL-cholesterol. On the contrary, we have not found statistically significant differences between the DD and MetS groups in the concentrations of serum insulin, glucose, HOMA and QUICKI indices, in CD-LDL and MAU. In this pilot study, we have found in patients with depressive disorder certain features of metabolic syndrome, especially insulin resistance and oxidative stress.

Cyclic fatigue resistance of ProTaper Next nickel-titanium rotary files.

PubMed

Elnaghy, A M

2014-11-01

To compare the cyclic fatigue resistance of ProTaper Next files (PTN; Dentsply Maillefer, Ballaigues, Switzerland) with Twisted Files (TF; SybronEndo, Orange, CA, USA), HyFlex CM (HF; ColténeEndo/Whaledent, Inc, Cuyahoga Falls, OH, USA) and ProTaper Universal (PT; Dentsply Maillefer). Size 25, .06 taper for PTN X2, TF, HF and PT F1 size 20, .07 taper were rotated in simulated canals until failure, and the number of cycles to failure (NCF) was recorded to evaluate their cyclic fatigue resistance. A scanning electron microscope was used to characterize the topographic features of the fracture surfaces of broken files. The data of the NCF and fragment length values were analysed statistically using one-way analysis of variance and Tukey post hoc tests. Statistical significance level was set at P < 0.05. Twisted Files had a significantly higher resistance to cyclic fatigue than the other instruments (P < 0.05). No significant difference was found in NCF between PTN and HF (P > 0.05); however, there was a significant difference (P < 0.05) of both these systems with PT, which exhibited the lowest mean NCF. The ranking in the NCF values was: TF > PTN > HF > PT. The fracture cross-sections of all brands revealed similar fractographic features, including crack origins, fatigue zone and an overload fast fracture zone. The new ProTaper Next had greater resistance to cyclic fatigue compared with ProTaper and HyFlex CM but not the Twisted Files. © 2014 International Endodontic Journal. Published by John Wiley & Sons Ltd.

The writer independent online handwriting recognition system frog on hand and cluster generative statistical dynamic time warping.

PubMed

Bahlmann, Claus; Burkhardt, Hans

2004-03-01

In this paper, we give a comprehensive description of our writer-independent online handwriting recognition system frog on hand. The focus of this work concerns the presentation of the classification/training approach, which we call cluster generative statistical dynamic time warping (CSDTW). CSDTW is a general, scalable, HMM-based method for variable-sized, sequential data that holistically combines cluster analysis and statistical sequence modeling. It can handle general classification problems that rely on this sequential type of data, e.g., speech recognition, genome processing, robotics, etc. Contrary to previous attempts, clustering and statistical sequence modeling are embedded in a single feature space and use a closely related distance measure. We show character recognition experiments of frog on hand using CSDTW on the UNIPEN online handwriting database. The recognition accuracy is significantly higher than reported results of other handwriting recognition systems. Finally, we describe the real-time implementation of frog on hand on a Linux Compaq iPAQ embedded device.

Comparative evaluation of features and techniques for identifying activity type and estimating energy cost from accelerometer data

PubMed Central

Kate, Rohit J.; Swartz, Ann M.; Welch, Whitney A.; Strath, Scott J.

2016-01-01

Wearable accelerometers can be used to objectively assess physical activity. However, the accuracy of this assessment depends on the underlying method used to process the time series data obtained from accelerometers. Several methods have been proposed that use this data to identify the type of physical activity and estimate its energy cost. Most of the newer methods employ some machine learning technique along with suitable features to represent the time series data. This paper experimentally compares several of these techniques and features on a large dataset of 146 subjects doing eight different physical activities wearing an accelerometer on the hip. Besides features based on statistics, distance based features and simple discrete features straight from the time series were also evaluated. On the physical activity type identification task, the results show that using more features significantly improve results. Choice of machine learning technique was also found to be important. However, on the energy cost estimation task, choice of features and machine learning technique were found to be less influential. On that task, separate energy cost estimation models trained specifically for each type of physical activity were found to be more accurate than a single model trained for all types of physical activities. PMID:26862679

Analysis of DCE-MRI features in tumor and the surrounding stroma for prediction of Ki-67 proliferation status in breast cancer

NASA Astrophysics Data System (ADS)

Li, Hui; Fan, Ming; Zhang, Peng; Li, Yuanzhe; Cheng, Hu; Zhang, Juan; Shao, Guoliang; Li, Lihua

2018-03-01

Breast cancer, with its high heterogeneity, is the most common malignancies in women. In addition to the entire tumor itself, tumor microenvironment could also play a fundamental role on the occurrence and development of tumors. The aim of this study is to investigate the role of heterogeneity within a tumor and the surrounding stromal tissue in predicting the Ki-67 proliferation status of oestrogen receptor (ER)-positive breast cancer patients. To this end, we collected 62 patients imaged with preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for analysis. The tumor and the peritumoral stromal tissue were segmented into 8 shells with 5 mm width outside of tumor. The mean enhancement rate in the stromal shells showed a decreasing order if their distances to the tumor increase. Statistical and texture features were extracted from the tumor and the surrounding stromal bands, and multivariate logistic regression classifiers were trained and tested based on these features. An area under the receiver operating characteristic curve (AUC) were calculated to evaluate performance of the classifiers. Furthermore, the statistical model using features extracted from boundary shell next to the tumor produced AUC of 0.796+/-0.076, which is better than that using features from the other subregions. Furthermore, the prediction model using 7 features from the entire tumor produced an AUC value of 0.855+/-0.065. The classifier based on 9 selected features extracted from peritumoral stromal region showed an AUC value of 0.870+/-0.050. Finally, after fusion of the predictive model obtained from entire tumor and the peritumoral stromal regions, the classifier performance was significantly improved with AUC of 0.920. The results indicated that heterogeneity in tumor boundary and peritumoral stromal region could be valuable in predicting the indicator associated with prognosis.

CAD scheme for detection of hemorrhages and exudates in ocular fundus images

NASA Astrophysics Data System (ADS)

Hatanaka, Yuji; Nakagawa, Toshiaki; Hayashi, Yoshinori; Mizukusa, Yutaka; Fujita, Akihiro; Kakogawa, Masakatsu; Kawase, Kazuhide; Hara, Takeshi; Fujita, Hiroshi

2007-03-01

This paper describes a method for detecting hemorrhages and exudates in ocular fundus images. The detection of hemorrhages and exudates is important in order to diagnose diabetic retinopathy. Diabetic retinopathy is one of the most significant factors contributing to blindness, and early detection and treatment are important. In this study, hemorrhages and exudates were automatically detected in fundus images without using fluorescein angiograms. Subsequently, the blood vessel regions incorrectly detected as hemorrhages were eliminated by first examining the structure of the blood vessels and then evaluating the length-to-width ratio. Finally, the false positives were eliminated by checking the following features extracted from candidate images: the number of pixels, contrast, 13 features calculated from the co-occurrence matrix, two features based on gray-level difference statistics, and two features calculated from the extrema method. The sensitivity of detecting hemorrhages in the fundus images was 85% and that of detecting exudates was 77%. Our fully automated scheme could accurately detect hemorrhages and exudates.

Tissue classification using depth-dependent ultrasound time series analysis: in-vitro animal study

NASA Astrophysics Data System (ADS)

Imani, Farhad; Daoud, Mohammad; Moradi, Mehdi; Abolmaesumi, Purang; Mousavi, Parvin

2011-03-01

Time series analysis of ultrasound radio-frequency (RF) signals has been shown to be an effective tissue classification method. Previous studies of this method for tissue differentiation at high and clinical-frequencies have been reported. In this paper, analysis of RF time series is extended to improve tissue classification at the clinical frequencies by including novel features extracted from the time series spectrum. The primary feature examined is the Mean Central Frequency (MCF) computed for regions of interest (ROIs) in the tissue extending along the axial axis of the transducer. In addition, the intercept and slope of a line fitted to the MCF-values of the RF time series as a function of depth have been included. To evaluate the accuracy of the new features, an in vitro animal study is performed using three tissue types: bovine muscle, bovine liver, and chicken breast, where perfect two-way classification is achieved. The results show statistically significant improvements over the classification accuracies with previously reported features.

Characterizing multivariate decoding models based on correlated EEG spectral features

PubMed Central

McFarland, Dennis J.

2013-01-01

Objective Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Methods Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). Results The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Conclusions Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. Significance While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. PMID:23466267

Cardiometabolic disease and features of depression and bipolar disorder: population-based, cross-sectional study.

PubMed

Martin, Daniel J; Ul-Haq, Zia; Nicholl, Barbara I; Cullen, Breda; Evans, Jonathan; Gill, Jason M R; Roberts, Beverly; Gallacher, John; Mackay, Daniel; McIntosh, Andrew; Hotopf, Matthew; Craddock, Nick; Deary, Ian J; Pell, Jill P; Smith, Daniel J

2016-04-01

The relative contribution of demographic, lifestyle and medication factors to the association between affective disorders and cardiometabolic diseases is poorly understood. To assess the relationship between cardiometabolic disease and features of depresion and bipolar disorder within a large population sample. Cross-sectional study of 145 991 UK Biobank participants: multivariate analyses of associations between features of depression or bipolar disorder and five cardiometabolic outcomes, adjusting for confounding factors. There were significant associations between mood disorder features and 'any cardiovascular disease' (depression odds ratio (OR) = 1.15, 95% CI 1.12-1.19; bipolar OR = 1.28, 95% CI 1.14-1.43) and with hypertension (depression OR = 1.15, 95% CI 1.13-1.18; bipolar OR = 1.26, 95% CI 1.12-1.42). Individuals with features of mood disorder taking psychotropic medication were significantly more likely than controls not on psychotropics to report myocardial infarction (depression OR = 1.47, 95% CI 1.24-1.73; bipolar OR = 2.23, 95% CI 1.53-3.57) and stroke (depression OR = 2.46, 95% CI 2.10-2.80; bipolar OR = 2.31, 95% CI 1.39-3.85). Associations between features of depression or bipolar disorder and cardiovascular disease outcomes were statistically independent of demographic, lifestyle and medication confounders. Psychotropic medication may also be a risk factor for cardiometabolic disease in individuals without a clear history of mood disorder. © The Royal College of Psychiatrists 2016.

Efficient Feature Selection and Classification of Protein Sequence Data in Bioinformatics

PubMed Central

Faye, Ibrahima; Samir, Brahim Belhaouari; Md Said, Abas

2014-01-01

Bioinformatics has been an emerging area of research for the last three decades. The ultimate aims of bioinformatics were to store and manage the biological data, and develop and analyze computational tools to enhance their understanding. The size of data accumulated under various sequencing projects is increasing exponentially, which presents difficulties for the experimental methods. To reduce the gap between newly sequenced protein and proteins with known functions, many computational techniques involving classification and clustering algorithms were proposed in the past. The classification of protein sequences into existing superfamilies is helpful in predicting the structure and function of large amount of newly discovered proteins. The existing classification results are unsatisfactory due to a huge size of features obtained through various feature encoding methods. In this work, a statistical metric-based feature selection technique has been proposed in order to reduce the size of the extracted feature vector. The proposed method of protein classification shows significant improvement in terms of performance measure metrics: accuracy, sensitivity, specificity, recall, F-measure, and so forth. PMID:25045727

Application of machine vision to pup loaf bread evaluation

NASA Astrophysics Data System (ADS)

Zayas, Inna Y.; Chung, O. K.

1996-12-01

Intrinsic end-use quality of hard winter wheat breeding lines is routinely evaluated at the USDA, ARS, USGMRL, Hard Winter Wheat Quality Laboratory. Experimental baking test of pup loaves is the ultimate test for evaluating hard wheat quality. Computer vision was applied to developing an objective methodology for bread quality evaluation for the 1994 and 1995 crop wheat breeding line samples. Computer extracted features for bread crumb grain were studied, using subimages (32 by 32 pixel) and features computed for the slices with different threshold settings. A subsampling grid was located with respect to the axis of symmetry of a slice to provide identical topological subimage information. Different ranking techniques were applied to the databases. Statistical analysis was run on the database with digital image and breadmaking features. Several ranking algorithms and data visualization techniques were employed to create a sensitive scale for porosity patterns of bread crumb. There were significant linear correlations between machine vision extracted features and breadmaking parameters. Crumb grain scores by human experts were correlated more highly with some image features than with breadmaking parameters.

Enhanced Higgs boson to τ(+)τ(-) search with deep learning.

PubMed

Baldi, P; Sadowski, P; Whiteson, D

2015-03-20

The Higgs boson is thought to provide the interaction that imparts mass to the fundamental fermions, but while measurements at the Large Hadron Collider (LHC) are consistent with this hypothesis, current analysis techniques lack the statistical power to cross the traditional 5σ significance barrier without more data. Deep learning techniques have the potential to increase the statistical power of this analysis by automatically learning complex, high-level data representations. In this work, deep neural networks are used to detect the decay of the Higgs boson to a pair of tau leptons. A Bayesian optimization algorithm is used to tune the network architecture and training algorithm hyperparameters, resulting in a deep network of eight nonlinear processing layers that improves upon the performance of shallow classifiers even without the use of features specifically engineered by physicists for this application. The improvement in discovery significance is equivalent to an increase in the accumulated data set of 25%.

Evidence for variability of the hard X-ray feature in the Hercules X-1 energy spectrum

NASA Technical Reports Server (NTRS)

Tueller, J.; Cline, T. L.; Teegarden, B. J.; Paciesas, W. S.; Boclet, D.; Durochoux, P.; Hameury, J. M.; Prantzos, N.; Haymes, R. C.

1983-01-01

The hard X-ray spectrum of HER X-1 was measured for the first time with a high resolution (1.4 keV FWHM) germanium spectrometer. The observation was performed near the peak of the on-state in the 35 day cycle and the 1.24 pulsations were observed between the energies of 20 keV and 70 keV. The feature corresponds to an excess of 7.5 sigma over the low energy continuum. Smooth continuum models are poor fits to the entire energy range (chance probabilities of 2 percent or less). The best fit energies are 35 keV for an absorption line and 39 keV for an emission line. These are significantly lower energies than those derived from previous experiments. A direct comparison of our data with the results of the MPI/AIT group shows statistically significant variations which strongly suggest variability in the source.

Toward optimal feature and time segment selection by divergence method for EEG signals classification.

PubMed

Wang, Jie; Feng, Zuren; Lu, Na; Luo, Jing

2018-06-01

Feature selection plays an important role in the field of EEG signals based motor imagery pattern classification. It is a process that aims to select an optimal feature subset from the original set. Two significant advantages involved are: lowering the computational burden so as to speed up the learning procedure and removing redundant and irrelevant features so as to improve the classification performance. Therefore, feature selection is widely employed in the classification of EEG signals in practical brain-computer interface systems. In this paper, we present a novel statistical model to select the optimal feature subset based on the Kullback-Leibler divergence measure, and automatically select the optimal subject-specific time segment. The proposed method comprises four successive stages: a broad frequency band filtering and common spatial pattern enhancement as preprocessing, features extraction by autoregressive model and log-variance, the Kullback-Leibler divergence based optimal feature and time segment selection and linear discriminate analysis classification. More importantly, this paper provides a potential framework for combining other feature extraction models and classification algorithms with the proposed method for EEG signals classification. Experiments on single-trial EEG signals from two public competition datasets not only demonstrate that the proposed method is effective in selecting discriminative features and time segment, but also show that the proposed method yields relatively better classification results in comparison with other competitive methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

Active contours on statistical manifolds and texture segmentation

Treesearch

Sang-Mook Lee; A. Lynn Abbott; Neil A. Clark; Philip A. Araman

2005-01-01

A new approach to active contours on statistical manifolds is presented. The statistical manifolds are 2- dimensional Riemannian manifolds that are statistically defined by maps that transform a parameter domain onto a set of probability density functions. In this novel framework, color or texture features are measured at each image point and their statistical...

Active contours on statistical manifolds and texture segmentaiton

Treesearch

Sang-Mook Lee; A. Lynn Abbott; Neil A. Clark; Philip A. Araman

2005-01-01

A new approach to active contours on statistical manifolds is presented. The statistical manifolds are 2- dimensional Riemannian manifolds that are statistically defined by maps that transform a parameter domain onto-a set of probability density functions. In this novel framework, color or texture features are measured at each Image point and their statistical...

A comparison study of image features between FFDM and film mammogram images

PubMed Central

Jing, Hao; Yang, Yongyi; Wernick, Miles N.; Yarusso, Laura M.; Nishikawa, Robert M.

2012-01-01

Purpose: This work is to provide a direct, quantitative comparison of image features measured by film and full-field digital mammography (FFDM). The purpose is to investigate whether there is any systematic difference between film and FFDM in terms of quantitative image features and their influence on the performance of a computer-aided diagnosis (CAD) system. Methods: The authors make use of a set of matched film-FFDM image pairs acquired from cadaver breast specimens with simulated microcalcifications consisting of bone and teeth fragments using both a GE digital mammography system and a screen-film system. To quantify the image features, the authors consider a set of 12 textural features of lesion regions and six image features of individual microcalcifications (MCs). The authors first conduct a direct comparison on these quantitative features extracted from film and FFDM images. The authors then study the performance of a CAD classifier for discriminating between MCs and false positives (FPs) when the classifier is trained on images of different types (film, FFDM, or both). Results: For all the features considered, the quantitative results show a high degree of correlation between features extracted from film and FFDM, with the correlation coefficients ranging from 0.7326 to 0.9602 for the different features. Based on a Fisher sign rank test, there was no significant difference observed between the features extracted from film and those from FFDM. For both MC detection and discrimination of FPs from MCs, FFDM had a slight but statistically significant advantage in performance; however, when the classifiers were trained on different types of images (acquired with FFDM or SFM) for discriminating MCs from FPs, there was little difference. Conclusions: The results indicate good agreement between film and FFDM in quantitative image features. While FFDM images provide better detection performance in MCs, FFDM and film images may be interchangeable for the purposes of training CAD algorithms, and a single CAD algorithm may be applied to either type of images. PMID:22830771

Anger is a distinctive feature of epilepsy patients with depression.

PubMed

Mori, Yasuhiro; Kanemoto, Kousuke; Onuma, Teiichi; Tanaka, Masaki; Oshima, Tomohiro; Kato, Hiroko; Tachimori, Hisateru; Wada, Kazumaru; Kikuchi, Takashi; Tomita, Tetsu; Chen, Lei; Fang, Liu; Yoshida, Shuichi; Kato, Masaaki; Kaneko, Sunao

2014-02-01

Controversy exists regarding the similarity between depression as seen in patients with epilepsy and in those with idiopathic major depression. The objective of this study was to examine whether anger is a distinctive feature of depression in epilepsy. Participants included 487 adult patients with epilepsy (study group) and 85 patients with idiopathic major depression according to Diagnostic and Statistical Manual of Mental Disorders Fourth Edition (DSM-IV) criteria, and without other neurological complications (control group). All participants completed the Inventory of Depressive Symptomatology Self-Report (IDS-SR) and the Buss-Perry Aggression Questionnaire (BAQ). The IDS-SR is a self-report questionnaire that measures depression severity and assesses all symptoms of depression as defined by the DSM-IV. The BAQ is a self-rating scale designed for assessing aggression. After examining potential confounding factors (i.e., demographic and clinical variables) using a multivariate linear regression model, BAQ scores were compared between the study (n = 85) and control groups (n = 54) for patients with moderate or severe depression using established cut-off points (IDS-SR score > 25). BAQ scores were significantly higher in the study group (P = 0.009). Among the BAQ subscales, only anger showed a statistically significant difference (P = 0.013). Although a significant correlation was revealed between the IDS-SR and BAQ scores in the study group, no such correlation was found in the control group. Thus, anger might be a constituent component of depression among epilepsy patients, but not among idiopathic major depression patients.

[Detection of ALK, ROS1 and RET fusion genes in non-small cell lung cancer patients and its clinicopathologic correlation].

PubMed

Zhong, Shan; Zhang, Haiping; Bai, Dongyu; Gao, Dehong; Zheng, Jie; Ding, Yi

2015-09-01

To study the prevalence of ALK, ROS1 and RET fusion genes in non-small cell lung cancer (NSCLC), and its correlation with clinicopathologic features. Formalin-fixed and paraffin-embedded tissue sections from samples of 302 patients with NSCLC were screened for ALK, ROS1, RET fusions by real-time polymerase chain reaction (PCR). All of the cases were validated by Sanger DNA sequencing. The relationship between ALK, ROS1, RET fusion genes and clinicopathologic features were analyzed. In the cohort of 302 NSCLC samples, 3.97% (12/302) were found to contain ALK fusion genes, including 3 cases with E13; A20 gene fusion, 3 cases with E6; A20 gene fusion and 3 cases with E20; A20 gene fusion. There was no statistically significant difference in patient's gender, age, smoking history and histologic type. Moreover, in the 302 NSCLC samples studied, 3.97% (12/302) were found to contain ROS1 fusion genes, with CD74-ROS1 fusion identified in 9 cases. There was no statistically significant difference in patients' gender, age, smoking history and histologic type. One non-smoking elderly female patient with pulmonary adenocarcinoma had RET gene fusion. None of the cases studied had concurrent ALK, ROS1 and RET mutations. The ALK, ROS1 and RET fusion gene mutation rates in NSCLC are low, they represent some specific molecular subtypes of NSCLC. Genetic testing has significant meaning to guide clinical targeted therapy.

GuiTope: an application for mapping random-sequence peptides to protein sequences.

PubMed

Halperin, Rebecca F; Stafford, Phillip; Emery, Jack S; Navalkar, Krupa Arun; Johnston, Stephen Albert

2012-01-03

Random-sequence peptide libraries are a commonly used tool to identify novel ligands for binding antibodies, other proteins, and small molecules. It is often of interest to compare the selected peptide sequences to the natural protein binding partners to infer the exact binding site or the importance of particular residues. The ability to search a set of sequences for similarity to a set of peptides may sometimes enable the prediction of an antibody epitope or a novel binding partner. We have developed a software application designed specifically for this task. GuiTope provides a graphical user interface for aligning peptide sequences to protein sequences. All alignment parameters are accessible to the user including the ability to specify the amino acid frequency in the peptide library; these frequencies often differ significantly from those assumed by popular alignment programs. It also includes a novel feature to align di-peptide inversions, which we have found improves the accuracy of antibody epitope prediction from peptide microarray data and shows utility in analyzing phage display datasets. Finally, GuiTope can randomly select peptides from a given library to estimate a null distribution of scores and calculate statistical significance. GuiTope provides a convenient method for comparing selected peptide sequences to protein sequences, including flexible alignment parameters, novel alignment features, ability to search a database, and statistical significance of results. The software is available as an executable (for PC) at http://www.immunosignature.com/software and ongoing updates and source code will be available at sourceforge.net.

Towards Accurate Modelling of Galaxy Clustering on Small Scales: Testing the Standard ΛCDM + Halo Model

NASA Astrophysics Data System (ADS)

Sinha, Manodeep; Berlind, Andreas A.; McBride, Cameron K.; Scoccimarro, Roman; Piscionere, Jennifer A.; Wibking, Benjamin D.

2018-04-01

Interpreting the small-scale clustering of galaxies with halo models can elucidate the connection between galaxies and dark matter halos. Unfortunately, the modelling is typically not sufficiently accurate for ruling out models statistically. It is thus difficult to use the information encoded in small scales to test cosmological models or probe subtle features of the galaxy-halo connection. In this paper, we attempt to push halo modelling into the "accurate" regime with a fully numerical mock-based methodology and careful treatment of statistical and systematic errors. With our forward-modelling approach, we can incorporate clustering statistics beyond the traditional two-point statistics. We use this modelling methodology to test the standard ΛCDM + halo model against the clustering of SDSS DR7 galaxies. Specifically, we use the projected correlation function, group multiplicity function and galaxy number density as constraints. We find that while the model fits each statistic separately, it struggles to fit them simultaneously. Adding group statistics leads to a more stringent test of the model and significantly tighter constraints on model parameters. We explore the impact of varying the adopted halo definition and cosmological model and find that changing the cosmology makes a significant difference. The most successful model we tried (Planck cosmology with Mvir halos) matches the clustering of low luminosity galaxies, but exhibits a 2.3σ tension with the clustering of luminous galaxies, thus providing evidence that the "standard" halo model needs to be extended. This work opens the door to adding interesting freedom to the halo model and including additional clustering statistics as constraints.

Impact of feature saliency on visual category learning.

PubMed

Hammer, Rubi

2015-01-01

People have to sort numerous objects into a large number of meaningful categories while operating in varying contexts. This requires identifying the visual features that best predict the 'essence' of objects (e.g., edibility), rather than categorizing objects based on the most salient features in a given context. To gain this capacity, visual category learning (VCL) relies on multiple cognitive processes. These may include unsupervised statistical learning, that requires observing multiple objects for learning the statistics of their features. Other learning processes enable incorporating different sources of supervisory information, alongside the visual features of the categorized objects, from which the categorical relations between few objects can be deduced. These deductions enable inferring that objects from the same category may differ from one another in some high-saliency feature dimensions, whereas lower-saliency feature dimensions can best differentiate objects from distinct categories. Here I illustrate how feature saliency affects VCL, by also discussing kinds of supervisory information enabling reflective categorization. Arguably, principles debated here are often being ignored in categorization studies.

Impact of feature saliency on visual category learning

PubMed Central

Hammer, Rubi

2015-01-01

People have to sort numerous objects into a large number of meaningful categories while operating in varying contexts. This requires identifying the visual features that best predict the ‘essence’ of objects (e.g., edibility), rather than categorizing objects based on the most salient features in a given context. To gain this capacity, visual category learning (VCL) relies on multiple cognitive processes. These may include unsupervised statistical learning, that requires observing multiple objects for learning the statistics of their features. Other learning processes enable incorporating different sources of supervisory information, alongside the visual features of the categorized objects, from which the categorical relations between few objects can be deduced. These deductions enable inferring that objects from the same category may differ from one another in some high-saliency feature dimensions, whereas lower-saliency feature dimensions can best differentiate objects from distinct categories. Here I illustrate how feature saliency affects VCL, by also discussing kinds of supervisory information enabling reflective categorization. Arguably, principles debated here are often being ignored in categorization studies. PMID:25954220

Characterizing microstructural features of biomedical samples by statistical analysis of Mueller matrix images

NASA Astrophysics Data System (ADS)

He, Honghui; Dong, Yang; Zhou, Jialing; Ma, Hui

2017-03-01

As one of the salient features of light, polarization contains abundant structural and optical information of media. Recently, as a comprehensive description of polarization property, the Mueller matrix polarimetry has been applied to various biomedical studies such as cancerous tissues detections. In previous works, it has been found that the structural information encoded in the 2D Mueller matrix images can be presented by other transformed parameters with more explicit relationship to certain microstructural features. In this paper, we present a statistical analyzing method to transform the 2D Mueller matrix images into frequency distribution histograms (FDHs) and their central moments to reveal the dominant structural features of samples quantitatively. The experimental results of porcine heart, intestine, stomach, and liver tissues demonstrate that the transformation parameters and central moments based on the statistical analysis of Mueller matrix elements have simple relationships to the dominant microstructural properties of biomedical samples, including the density and orientation of fibrous structures, the depolarization power, diattenuation and absorption abilities. It is shown in this paper that the statistical analysis of 2D images of Mueller matrix elements may provide quantitative or semi-quantitative criteria for biomedical diagnosis.

Deformable image registration as a tool to improve survival prediction after neoadjuvant chemotherapy for breast cancer: results from the ACRIN 6657/I-SPY-1 trial

NASA Astrophysics Data System (ADS)

Jahani, Nariman; Cohen, Eric; Hsieh, Meng-Kang; Weinstein, Susan P.; Pantalone, Lauren; Davatzikos, Christos; Kontos, Despina

2018-02-01

We examined the ability of DCE-MRI longitudinal features to give early prediction of recurrence-free survival (RFS) in women undergoing neoadjuvant chemotherapy for breast cancer, in a retrospective analysis of 106 women from the ISPY 1 cohort. These features were based on the voxel-wise changes seen in registered images taken before treatment and after the first round of chemotherapy. We computed the transformation field using a robust deformable image registration technique to match breast images from these two visits. Using the deformation field, parametric response maps (PRM) — a voxel-based feature analysis of longitudinal changes in images between visits — was computed for maps of four kinetic features (signal enhancement ratio, peak enhancement, and wash-in/wash-out slopes). A two-level discrete wavelet transform was applied to these PRMs to extract heterogeneity information about tumor change between visits. To estimate survival, a Cox proportional hazard model was applied with the C statistic as the measure of success in predicting RFS. The best PRM feature (as determined by C statistic in univariable analysis) was determined for each of the four kinetic features. The baseline model, incorporating functional tumor volume, age, race, and hormone response status, had a C statistic of 0.70 in predicting RFS. The model augmented with the four PRM features had a C statistic of 0.76. Thus, our results suggest that adding information on the texture of voxel-level changes in tumor kinetic response between registered images of first and second visits could improve early RFS prediction in breast cancer after neoadjuvant chemotherapy.

Lipid-lowering drugs (statins) and peripheral neuropathy.

PubMed

Emad, Mohammadreza; Arjmand, Hosein; Farpour, Hamid Reza; Kardeh, Bahareh

2018-03-01

Peripheral neuropathy is a disorder with often unknown causes. Some drugs, including statins, are proposed to be among the causes of peripheral neuropathy. This study aimed at evaluating this condition by electrodiagnostic study among patients who had received statins. This case-control study was conducted in Shiraz, Iran in 2015, and included 39 patients aged 35-55 who had received statins for at least 6 months, and 39 healthy matched controls. Using electrodiagnosis, the sensory and motor wave features (amplitude, latency and nerve conduction velocity) of the peripheral nerves (Median, Ulnar, Tibial, Sural, and Peroneal) were evaluated among the subjects. Data were analyzed using SPSS software and p<0.05 was considered statistically significant. Regarding the occurrence of neuropathy, there were no significant differences in any of the definitions presented for peripheral neuropathy. However, the difference was close to significance for one definition [2 abnormalities in 2 nerves (p=0.055)]. Regarding mean values of the features, significant differences were observed in two features: amplitude of the peroneal motor nerve (p=0.048) and amplitude of the sural sensory nerve (p=0.036). Since statins are widely used, awareness regarding their side-effects would lead to better treatment. Even though no significant differences were found between the groups regarding the occurrence of peripheral neuropathy, there were significant differences in amplitudes of the sural sensory response and the peroneal motor response. This indicates the involvement of peripheral nerves. Therefore, we recommend that patients and physicians should be informed about the possible symptoms of this condition.

A statistical shape modelling framework to extract 3D shape biomarkers from medical imaging data: assessing arch morphology of repaired coarctation of the aorta.

PubMed

Bruse, Jan L; McLeod, Kristin; Biglino, Giovanni; Ntsinjana, Hopewell N; Capelli, Claudio; Hsia, Tain-Yen; Sermesant, Maxime; Pennec, Xavier; Taylor, Andrew M; Schievano, Silvia

2016-05-31

Medical image analysis in clinical practice is commonly carried out on 2D image data, without fully exploiting the detailed 3D anatomical information that is provided by modern non-invasive medical imaging techniques. In this paper, a statistical shape analysis method is presented, which enables the extraction of 3D anatomical shape features from cardiovascular magnetic resonance (CMR) image data, with no need for manual landmarking. The method was applied to repaired aortic coarctation arches that present complex shapes, with the aim of capturing shape features as biomarkers of potential functional relevance. The method is presented from the user-perspective and is evaluated by comparing results with traditional morphometric measurements. Steps required to set up the statistical shape modelling analyses, from pre-processing of the CMR images to parameter setting and strategies to account for size differences and outliers, are described in detail. The anatomical mean shape of 20 aortic arches post-aortic coarctation repair (CoA) was computed based on surface models reconstructed from CMR data. By analysing transformations that deform the mean shape towards each of the individual patient's anatomy, shape patterns related to differences in body surface area (BSA) and ejection fraction (EF) were extracted. The resulting shape vectors, describing shape features in 3D, were compared with traditionally measured 2D and 3D morphometric parameters. The computed 3D mean shape was close to population mean values of geometric shape descriptors and visually integrated characteristic shape features associated with our population of CoA shapes. After removing size effects due to differences in body surface area (BSA) between patients, distinct 3D shape features of the aortic arch correlated significantly with EF (r = 0.521, p = .022) and were well in agreement with trends as shown by traditional shape descriptors. The suggested method has the potential to discover previously unknown 3D shape biomarkers from medical imaging data. Thus, it could contribute to improving diagnosis and risk stratification in complex cardiac disease.

Probabilistic reasoning under time pressure: an assessment in Italian, Spanish and English psychology undergraduates

NASA Astrophysics Data System (ADS)

Agus, M.; Hitchcott, P. K.; Penna, M. P.; Peró-Cebollero, M.; Guàrdia-Olmos, J.

2016-11-01

Many studies have investigated the features of probabilistic reasoning developed in relation to different formats of problem presentation, showing that it is affected by various individual and contextual factors. Incomplete understanding of the identity and role of these factors may explain the inconsistent evidence concerning the effect of problem presentation format. Thus, superior performance has sometimes been observed for graphically, rather than verbally, presented problems. The present study was undertaken to address this issue. Psychology undergraduates without any statistical expertise (N = 173 in Italy; N = 118 in Spain; N = 55 in England) were administered statistical problems in two formats (verbal-numerical and graphical-pictorial) under a condition of time pressure. Students also completed additional measures indexing several potentially relevant individual dimensions (statistical ability, statistical anxiety, attitudes towards statistics and confidence). Interestingly, a facilitatory effect of graphical presentation was observed in the Italian and Spanish samples but not in the English one. Significantly, the individual dimensions predicting statistical performance also differed between the samples, highlighting a different role of confidence. Hence, these findings confirm previous observations concerning problem presentation format while simultaneously highlighting the importance of individual dimensions.

The statistical average of optical properties for alumina particle cluster in aircraft plume

NASA Astrophysics Data System (ADS)

Li, Jingying; Bai, Lu; Wu, Zhensen; Guo, Lixin

2018-04-01

We establish a model for lognormal distribution of monomer radius and number of alumina particle clusters in plume. According to the Multi-Sphere T Matrix (MSTM) theory, we provide a method for finding the statistical average of optical properties for alumina particle clusters in plume, analyze the effect of different distributions and different detection wavelengths on the statistical average of optical properties for alumina particle cluster, and compare the statistical average optical properties under the alumina particle cluster model established in this study and those under three simplified alumina particle models. The calculation results show that the monomer number of alumina particle cluster and its size distribution have a considerable effect on its statistical average optical properties. The statistical average of optical properties for alumina particle cluster at common detection wavelengths exhibit obvious differences, whose differences have a great effect on modeling IR and UV radiation properties of plume. Compared with the three simplified models, the alumina particle cluster model herein features both higher extinction and scattering efficiencies. Therefore, we may find that an accurate description of the scattering properties of alumina particles in aircraft plume is of great significance in the study of plume radiation properties.

Study on Hybrid Image Search Technology Based on Texts and Contents

NASA Astrophysics Data System (ADS)

Wang, H. T.; Ma, F. L.; Yan, C.; Pan, H.

2018-05-01

Image search was studied first here based on texts and contents, respectively. The text-based image feature extraction was put forward by integrating the statistical and topic features in view of the limitation of extraction of keywords only by means of statistical features of words. On the other hand, a search-by-image method was put forward based on multi-feature fusion in view of the imprecision of the content-based image search by means of a single feature. The layered-searching method depended on primarily the text-based image search method and additionally the content-based image search was then put forward in view of differences between the text-based and content-based methods and their difficult direct fusion. The feasibility and effectiveness of the hybrid search algorithm were experimentally verified.

GAISE 2016 Promotes Statistical Literacy

ERIC Educational Resources Information Center

Schield, Milo

2017-01-01

In the 2005 Guidelines for Assessment and Instruction in Statistics Education (GAISE), statistical literacy featured as a primary goal. The 2016 revision eliminated statistical literacy as a stated goal. Although this looks like a rejection, this paper argues that by including multivariate thinking and--more importantly--confounding as recommended…

The roles of shared vs. distinctive conceptual features in lexical access

PubMed Central

Vieth, Harrison E.; McMahon, Katie L.; de Zubicaray, Greig I.

2014-01-01

Contemporary models of spoken word production assume conceptual feature sharing determines the speed with which objects are named in categorically-related contexts. However, statistical models of concept representation have also identified a role for feature distinctiveness, i.e., features that identify a single concept and serve to distinguish it quickly from other similar concepts. In three experiments we investigated whether distinctive features might explain reports of counter-intuitive semantic facilitation effects in the picture word interference (PWI) paradigm. In Experiment 1, categorically-related distractors matched in terms of semantic similarity ratings (e.g., zebra and pony) and manipulated with respect to feature distinctiveness (e.g., a zebra has stripes unlike other equine species) elicited interference effects of comparable magnitude. Experiments 2 and 3 investigated the role of feature distinctiveness with respect to reports of facilitated naming with part-whole distractor-target relations (e.g., a hump is a distinguishing part of a CAMEL, whereas knee is not, vs. an unrelated part such as plug). Related part distractors did not influence target picture naming latencies significantly when the part denoted by the related distractor was not visible in the target picture (whether distinctive or not; Experiment 2). When the part denoted by the related distractor was visible in the target picture, non-distinctive part distractors slowed target naming significantly at SOA of −150 ms (Experiment 3). Thus, our results show that semantic interference does occur for part-whole distractor-target relations in PWI, but only when distractors denote features shared with the target and other category exemplars. We discuss the implications of these results for some recently developed, novel accounts of lexical access in spoken word production. PMID:25278914

TU-A-12A-07: CT-Based Biomarkers to Characterize Lung Lesion: Effects of CT Dose, Slice Thickness and Reconstruction Algorithm Based Upon a Phantom Study

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

Zhao, B; Tan, Y; Tsai, W

2014-06-15

Purpose: Radiogenomics promises the ability to study cancer tumor genotype from the phenotype obtained through radiographic imaging. However, little attention has been paid to the sensitivity of image features, the image-based biomarkers, to imaging acquisition techniques. This study explores the impact of CT dose, slice thickness and reconstruction algorithm on measuring image features using a thorax phantom. Methods: Twentyfour phantom lesions of known volume (1 and 2mm), shape (spherical, elliptical, lobular and spicular) and density (-630, -10 and +100 HU) were scanned on a GE VCT at four doses (25, 50, 100, and 200 mAs). For each scan, six imagemore » series were reconstructed at three slice thicknesses of 5, 2.5 and 1.25mm with continuous intervals, using the lung and standard reconstruction algorithms. The lesions were segmented with an in-house 3D algorithm. Fifty (50) image features representing lesion size, shape, edge, and density distribution/texture were computed. Regression method was employed to analyze the effect of CT dose, slice of thickness and reconstruction algorithm on these features adjusting 3 confounding factors (size, density and shape of phantom lesions). Results: The coefficients of CT dose, slice thickness and reconstruction algorithm are presented in Table 1 in the supplementary material. No significant difference was found between the image features calculated on low dose CT scans (25mAs and 50mAs). About 50% texture features were found statistically different between low doses and high doses (100 and 200mAs). Significant differences were found for almost all features when calculated on 1.25mm, 2.5mm, and 5mm slice thickness images. Reconstruction algorithms significantly affected all density-based image features, but not morphological features. Conclusions: There is a great need to standardize the CT imaging protocols for radiogenomics study because CT dose, slice thickness and reconstruction algorithm impact quantitative image features to various degrees as our study has shown.« less

Morphological Integration of Soft-Tissue Facial Morphology in Down Syndrome and Siblings

PubMed Central

Starbuck, John; Reeves, Roger H.; Richtsmeier, Joan

2011-01-01

Down syndrome (DS), resulting from trisomy of chromosome 21, is the most common live-born human aneuploidy. The phenotypic expression of trisomy 21 produces variable, though characteristic, facial morphology. Although certain facial features have been documented quantitatively and qualitatively as characteristic of DS (e.g., epicanthic folds, macroglossia, and hypertelorism), all of these traits occur in other craniofacial conditions with an underlying genetic cause. We hypothesize that the typical DS face is integrated differently than the face of non-DS siblings, and that the pattern of morphological integration unique to individuals with DS will yield information about underlying developmental associations between facial regions. We statistically compared morphological integration patterns of immature DS faces (N = 53) with those of non-DS siblings (N = 54), aged 6–12 years using 31 distances estimated from 3D coordinate data representing 17 anthropometric landmarks recorded on 3D digital photographic images. Facial features are affected differentially in DS, as evidenced by statistically significant differences in integration both within and between facial regions. Our results suggest a differential affect of trisomy on facial prominences during craniofacial development. PMID:21996933

Morphological integration of soft-tissue facial morphology in Down Syndrome and siblings.

PubMed

Starbuck, John; Reeves, Roger H; Richtsmeier, Joan

2011-12-01

Down syndrome (DS), resulting from trisomy of chromosome 21, is the most common live-born human aneuploidy. The phenotypic expression of trisomy 21 produces variable, though characteristic, facial morphology. Although certain facial features have been documented quantitatively and qualitatively as characteristic of DS (e.g., epicanthic folds, macroglossia, and hypertelorism), all of these traits occur in other craniofacial conditions with an underlying genetic cause. We hypothesize that the typical DS face is integrated differently than the face of non-DS siblings, and that the pattern of morphological integration unique to individuals with DS will yield information about underlying developmental associations between facial regions. We statistically compared morphological integration patterns of immature DS faces (N = 53) with those of non-DS siblings (N = 54), aged 6-12 years using 31 distances estimated from 3D coordinate data representing 17 anthropometric landmarks recorded on 3D digital photographic images. Facial features are affected differentially in DS, as evidenced by statistically significant differences in integration both within and between facial regions. Our results suggest a differential affect of trisomy on facial prominences during craniofacial development. 2011 Wiley Periodicals, Inc.

Condenser: a statistical aggregation tool for multi-sample quantitative proteomic data from Matrix Science Mascot Distiller™.

PubMed

Knudsen, Anders Dahl; Bennike, Tue; Kjeldal, Henrik; Birkelund, Svend; Otzen, Daniel Erik; Stensballe, Allan

2014-05-30

We describe Condenser, a freely available, comprehensive open-source tool for merging multidimensional quantitative proteomics data from the Matrix Science Mascot Distiller Quantitation Toolbox into a common format ready for subsequent bioinformatic analysis. A number of different relative quantitation technologies, such as metabolic (15)N and amino acid stable isotope incorporation, label-free and chemical-label quantitation are supported. The program features multiple options for curative filtering of the quantified peptides, allowing the user to choose data quality thresholds appropriate for the current dataset, and ensure the quality of the calculated relative protein abundances. Condenser also features optional global normalization, peptide outlier removal, multiple testing and calculation of t-test statistics for highlighting and evaluating proteins with significantly altered relative protein abundances. Condenser provides an attractive addition to the gold-standard quantitative workflow of Mascot Distiller, allowing easy handling of larger multi-dimensional experiments. Source code, binaries, test data set and documentation are available at http://condenser.googlecode.com/. Copyright © 2014 Elsevier B.V. All rights reserved.

Segmentation of prostate boundaries from ultrasound images using statistical shape model.

PubMed

Shen, Dinggang; Zhan, Yiqiang; Davatzikos, Christos

2003-04-01

This paper presents a statistical shape model for the automatic prostate segmentation in transrectal ultrasound images. A Gabor filter bank is first used to characterize the prostate boundaries in ultrasound images in both multiple scales and multiple orientations. The Gabor features are further reconstructed to be invariant to the rotation of the ultrasound probe and incorporated in the prostate model as image attributes for guiding the deformable segmentation. A hierarchical deformation strategy is then employed, in which the model adaptively focuses on the similarity of different Gabor features at different deformation stages using a multiresolution technique, i.e., coarse features first and fine features later. A number of successful experiments validate the algorithm.

Finding Statistically Significant Communities in Networks

PubMed Central

Lancichinetti, Andrea; Radicchi, Filippo; Ramasco, José J.; Fortunato, Santo

2011-01-01

Community structure is one of the main structural features of networks, revealing both their internal organization and the similarity of their elementary units. Despite the large variety of methods proposed to detect communities in graphs, there is a big need for multi-purpose techniques, able to handle different types of datasets and the subtleties of community structure. In this paper we present OSLOM (Order Statistics Local Optimization Method), the first method capable to detect clusters in networks accounting for edge directions, edge weights, overlapping communities, hierarchies and community dynamics. It is based on the local optimization of a fitness function expressing the statistical significance of clusters with respect to random fluctuations, which is estimated with tools of Extreme and Order Statistics. OSLOM can be used alone or as a refinement procedure of partitions/covers delivered by other techniques. We have also implemented sequential algorithms combining OSLOM with other fast techniques, so that the community structure of very large networks can be uncovered. Our method has a comparable performance as the best existing algorithms on artificial benchmark graphs. Several applications on real networks are shown as well. OSLOM is implemented in a freely available software (http://www.oslom.org), and we believe it will be a valuable tool in the analysis of networks. PMID:21559480

Phenotypic characterization of glioblastoma identified through shape descriptors

NASA Astrophysics Data System (ADS)

Chaddad, Ahmad; Desrosiers, Christian; Toews, Matthew

2016-03-01

This paper proposes quantitatively describing the shape of glioblastoma (GBM) tissue phenotypes as a set of shape features derived from segmentations, for the purposes of discriminating between GBM phenotypes and monitoring tumor progression. GBM patients were identified from the Cancer Genome Atlas, and quantitative MR imaging data were obtained from the Cancer Imaging Archive. Three GBM tissue phenotypes are considered including necrosis, active tumor and edema/invasion. Volumetric tissue segmentations are obtained from registered T1˗weighted (T1˗WI) postcontrast and fluid-attenuated inversion recovery (FLAIR) MRI modalities. Shape features are computed from respective tissue phenotype segmentations, and a Kruskal-Wallis test was employed to select features capable of classification with a significance level of p < 0.05. Several classifier models are employed to distinguish phenotypes, where a leave-one-out cross-validation was performed. Eight features were found statistically significant for classifying GBM phenotypes with p <0.05, orientation is uninformative. Quantitative evaluations show the SVM results in the highest classification accuracy of 87.50%, sensitivity of 94.59% and specificity of 92.77%. In summary, the shape descriptors proposed in this work show high performance in predicting GBM tissue phenotypes. They are thus closely linked to morphological characteristics of GBM phenotypes and could potentially be used in a computer assisted labeling system.

Gender differences in shame in patients with binge-eating disorder.

PubMed

Jambekar, Sheila A; Masheb, Robin M; Grilo, Carlos M

2003-04-01

To examine the relationship between shame and the behavioral and attitudinal features of eating disorders in men and women diagnosed with binge-eating disorder (BED). Participants were 188 consecutively evaluated adults (38 men and 150 women) who met Diagnostic and Statistical Manual of Mental Disorders, 4th edition, criteria for BED. Participants were interviewed and completed a battery of measures assessing shame, behavioral and attitudinal features of eating disorders, and general psychological functioning. Shame did not differ significantly by gender and was not associated with BMI or binge-eating frequency. Shame was significantly associated with the attitudinal features of eating disorders, even after controlling for levels of depression and self-esteem. When considered separately by gender and controlling for depression and self-esteem, shame was associated with body dissatisfaction in men and with weight concern in women. Men and women with BED, who presented for treatment, reported similar levels of shame. Overall, while shame was related to attitudinal features, the specific associations differed by gender. For men, shame was related to how dissatisfied they felt with their bodies, whereas for women, shame was associated with concerns about weight. Interestingly, shame was not related to BMI or binge-eating frequency in men or women. These results provide preliminary support for self-conscious emotions playing different roles in men and women with BED.

Possible detection of an emission feature near 584 A in the direction of G191-B2B

NASA Technical Reports Server (NTRS)

Green, James; Bowyer, Stuart; Jelinsky, Patrick

1990-01-01

A possible spectral emission feature is reported in the direction of the nearby hot white dwarf G191-B2B at 581.5 + or - 6 A with a significance of 3.8 sigma. This emission has been identified as He I 584.3 A. The emission cannot be due to local geocoronal emission or interplanetary backscatter of solar He I 584 A emission because the feature is not detected in a nearby sky exposure. Possible sources for this emission are examined, including the photosphere of G191-B2B, the comparison star G191-B2A, and a possible nebulosity near or around G191-B2B. The parameters required to explain the emission are derived for each case. All of these explanations require unexpected physical conditions; hence we believe this result must receive confirming verification despite the statistical likelihood of the detection.

Discrimination of inflammatory bowel disease using Raman spectroscopy and linear discriminant analysis methods

NASA Astrophysics Data System (ADS)

Ding, Hao; Cao, Ming; DuPont, Andrew W.; Scott, Larry D.; Guha, Sushovan; Singhal, Shashideep; Younes, Mamoun; Pence, Isaac; Herline, Alan; Schwartz, David; Xu, Hua; Mahadevan-Jansen, Anita; Bi, Xiaohong

2016-03-01

Inflammatory bowel disease (IBD) is an idiopathic disease that is typically characterized by chronic inflammation of the gastrointestinal tract. Recently much effort has been devoted to the development of novel diagnostic tools that can assist physicians for fast, accurate, and automated diagnosis of the disease. Previous research based on Raman spectroscopy has shown promising results in differentiating IBD patients from normal screening cases. In the current study, we examined IBD patients in vivo through a colonoscope-coupled Raman system. Optical diagnosis for IBD discrimination was conducted based on full-range spectra using multivariate statistical methods. Further, we incorporated several feature selection methods in machine learning into the classification model. The diagnostic performance for disease differentiation was significantly improved after feature selection. Our results showed that improved IBD diagnosis can be achieved using Raman spectroscopy in combination with multivariate analysis and feature selection.

Fly Photoreceptors Encode Phase Congruency

PubMed Central

Friederich, Uwe; Billings, Stephen A.; Hardie, Roger C.; Juusola, Mikko; Coca, Daniel

2016-01-01

More than five decades ago it was postulated that sensory neurons detect and selectively enhance behaviourally relevant features of natural signals. Although we now know that sensory neurons are tuned to efficiently encode natural stimuli, until now it was not clear what statistical features of the stimuli they encode and how. Here we reverse-engineer the neural code of Drosophila photoreceptors and show for the first time that photoreceptors exploit nonlinear dynamics to selectively enhance and encode phase-related features of temporal stimuli, such as local phase congruency, which are invariant to changes in illumination and contrast. We demonstrate that to mitigate for the inherent sensitivity to noise of the local phase congruency measure, the nonlinear coding mechanisms of the fly photoreceptors are tuned to suppress random phase signals, which explains why photoreceptor responses to naturalistic stimuli are significantly different from their responses to white noise stimuli. PMID:27336733

Statistical process control using optimized neural networks: a case study.

PubMed

Addeh, Jalil; Ebrahimzadeh, Ata; Azarbad, Milad; Ranaee, Vahid

2014-09-01

The most common statistical process control (SPC) tools employed for monitoring process changes are control charts. A control chart demonstrates that the process has altered by generating an out-of-control signal. This study investigates the design of an accurate system for the control chart patterns (CCPs) recognition in two aspects. First, an efficient system is introduced that includes two main modules: feature extraction module and classifier module. In the feature extraction module, a proper set of shape features and statistical feature are proposed as the efficient characteristics of the patterns. In the classifier module, several neural networks, such as multilayer perceptron, probabilistic neural network and radial basis function are investigated. Based on an experimental study, the best classifier is chosen in order to recognize the CCPs. Second, a hybrid heuristic recognition system is introduced based on cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier. The simulation results show that the proposed algorithm has high recognition accuracy. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

MedlinePlus FAQ: Statistics about MedlinePlus

MedlinePlus

... faq/stats.html Can you give me some statistics about MedlinePlus? To use the sharing features on ... For page requests and unique visitors, see MedlinePlus statistics . Return to the list of MedlinePlus FAQs About ...

Features of statistical dynamics in a finite system

NASA Astrophysics Data System (ADS)

Yan, Shiwei; Sakata, Fumihiko; Zhuo, Yizhong

2002-03-01

We study features of statistical dynamics in a finite Hamilton system composed of a relevant one degree of freedom coupled to an irrelevant multidegree of freedom system through a weak interaction. Special attention is paid on how the statistical dynamics changes depending on the number of degrees of freedom in the irrelevant system. It is found that the macrolevel statistical aspects are strongly related to an appearance of the microlevel chaotic motion, and a dissipation of the relevant motion is realized passing through three distinct stages: dephasing, statistical relaxation, and equilibrium regimes. It is clarified that the dynamical description and the conventional transport approach provide us with almost the same macrolevel and microlevel mechanisms only for the system with a very large number of irrelevant degrees of freedom. It is also shown that the statistical relaxation in the finite system is an anomalous diffusion and the fluctuation effects have a finite correlation time.

Features of statistical dynamics in a finite system.

PubMed

Yan, Shiwei; Sakata, Fumihiko; Zhuo, Yizhong

2002-03-01

We study features of statistical dynamics in a finite Hamilton system composed of a relevant one degree of freedom coupled to an irrelevant multidegree of freedom system through a weak interaction. Special attention is paid on how the statistical dynamics changes depending on the number of degrees of freedom in the irrelevant system. It is found that the macrolevel statistical aspects are strongly related to an appearance of the microlevel chaotic motion, and a dissipation of the relevant motion is realized passing through three distinct stages: dephasing, statistical relaxation, and equilibrium regimes. It is clarified that the dynamical description and the conventional transport approach provide us with almost the same macrolevel and microlevel mechanisms only for the system with a very large number of irrelevant degrees of freedom. It is also shown that the statistical relaxation in the finite system is an anomalous diffusion and the fluctuation effects have a finite correlation time.

Neural Systems with Numerically Matched Input-Output Statistic: Isotonic Bivariate Statistical Modeling

PubMed Central

Fiori, Simone

2007-01-01

Bivariate statistical modeling from incomplete data is a useful statistical tool that allows to discover the model underlying two data sets when the data in the two sets do not correspond in size nor in ordering. Such situation may occur when the sizes of the two data sets do not match (i.e., there are “holes” in the data) or when the data sets have been acquired independently. Also, statistical modeling is useful when the amount of available data is enough to show relevant statistical features of the phenomenon underlying the data. We propose to tackle the problem of statistical modeling via a neural (nonlinear) system that is able to match its input-output statistic to the statistic of the available data sets. A key point of the new implementation proposed here is that it is based on look-up-table (LUT) neural systems, which guarantee a computationally advantageous way of implementing neural systems. A number of numerical experiments, performed on both synthetic and real-world data sets, illustrate the features of the proposed modeling procedure. PMID:18566641

Segmenting lung fields in serial chest radiographs using both population-based and patient-specific shape statistics.

PubMed

Shi, Y; Qi, F; Xue, Z; Chen, L; Ito, K; Matsuo, H; Shen, D

2008-04-01

This paper presents a new deformable model using both population-based and patient-specific shape statistics to segment lung fields from serial chest radiographs. There are two novelties in the proposed deformable model. First, a modified scale invariant feature transform (SIFT) local descriptor, which is more distinctive than the general intensity and gradient features, is used to characterize the image features in the vicinity of each pixel. Second, the deformable contour is constrained by both population-based and patient-specific shape statistics, and it yields more robust and accurate segmentation of lung fields for serial chest radiographs. In particular, for segmenting the initial time-point images, the population-based shape statistics is used to constrain the deformable contour; as more subsequent images of the same patient are acquired, the patient-specific shape statistics online collected from the previous segmentation results gradually takes more roles. Thus, this patient-specific shape statistics is updated each time when a new segmentation result is obtained, and it is further used to refine the segmentation results of all the available time-point images. Experimental results show that the proposed method is more robust and accurate than other active shape models in segmenting the lung fields from serial chest radiographs.

A Generic multi-dimensional feature extraction method using multiobjective genetic programming.

PubMed

Zhang, Yang; Rockett, Peter I

2009-01-01

In this paper, we present a generic feature extraction method for pattern classification using multiobjective genetic programming. This not only evolves the (near-)optimal set of mappings from a pattern space to a multi-dimensional decision space, but also simultaneously optimizes the dimensionality of that decision space. The presented framework evolves vector-to-vector feature extractors that maximize class separability. We demonstrate the efficacy of our approach by making statistically-founded comparisons with a wide variety of established classifier paradigms over a range of datasets and find that for most of the pairwise comparisons, our evolutionary method delivers statistically smaller misclassification errors. At very worst, our method displays no statistical difference in a few pairwise comparisons with established classifier/dataset combinations; crucially, none of the misclassification results produced by our method is worse than any comparator classifier. Although principally focused on feature extraction, feature selection is also performed as an implicit side effect; we show that both feature extraction and selection are important to the success of our technique. The presented method has the practical consequence of obviating the need to exhaustively evaluate a large family of conventional classifiers when faced with a new pattern recognition problem in order to attain a good classification accuracy.

Unconscious analyses of visual scenes based on feature conjunctions.

PubMed

Tachibana, Ryosuke; Noguchi, Yasuki

2015-06-01

To efficiently process a cluttered scene, the visual system analyzes statistical properties or regularities of visual elements embedded in the scene. It is controversial, however, whether those scene analyses could also work for stimuli unconsciously perceived. Here we show that our brain performs the unconscious scene analyses not only using a single featural cue (e.g., orientation) but also based on conjunctions of multiple visual features (e.g., combinations of color and orientation information). Subjects foveally viewed a stimulus array (duration: 50 ms) where 4 types of bars (red-horizontal, red-vertical, green-horizontal, and green-vertical) were intermixed. Although a conscious perception of those bars was inhibited by a subsequent mask stimulus, the brain correctly analyzed the information about color, orientation, and color-orientation conjunctions of those invisible bars. The information of those features was then used for the unconscious configuration analysis (statistical processing) of the central bars, which induced a perceptual bias and illusory feature binding in visible stimuli at peripheral locations. While statistical analyses and feature binding are normally 2 key functions of the visual system to construct coherent percepts of visual scenes, our results show that a high-level analysis combining those 2 functions is correctly performed by unconscious computations in the brain. (c) 2015 APA, all rights reserved).

Statistical evolution of quiet-Sun small-scale magnetic features using Sunrise observations

NASA Astrophysics Data System (ADS)

Anusha, L. S.; Solanki, S. K.; Hirzberger, J.; Feller, A.

2017-02-01

The evolution of small magnetic features in quiet regions of the Sun provides a unique window for probing solar magneto-convection. Here we analyze small-scale magnetic features in the quiet Sun, using the high resolution, seeing-free observations from the Sunrise balloon borne solar observatory. Our aim is to understand the contribution of different physical processes, such as splitting, merging, emergence and cancellation of magnetic fields to the rearrangement, addition and removal of magnetic flux in the photosphere. We have employed a statistical approach for the analysis and the evolution studies are carried out using a feature-tracking technique. In this paper we provide a detailed description of the feature-tracking algorithm that we have newly developed and we present the results of a statistical study of several physical quantities. The results on the fractions of the flux in the emergence, appearance, splitting, merging, disappearance and cancellation qualitatively agrees with other recent studies. To summarize, the total flux gained in unipolar appearance is an order of magnitude larger than the total flux gained in emergence. On the other hand, the bipolar cancellation contributes nearly an equal amount to the loss of magnetic flux as unipolar disappearance. The total flux lost in cancellation is nearly six to eight times larger than the total flux gained in emergence. One big difference between our study and previous similar studies is that, thanks to the higher spatial resolution of Sunrise, we can track features with fluxes as low as 9 × 1014 Mx. This flux is nearly an order of magnitude lower than the smallest fluxes of the features tracked in the highest resolution previous studies based on Hinode data. The area and flux of the magnetic features follow power-law type distribution, while the lifetimes show either power-law or exponential type distribution depending on the exact definitions used to define various birth and death events. We have also statistically determined the evolution of the flux within the features in the course of their lifetime, finding that this evolution depends very strongly on the birth and death process that the features undergo.

Strategies for Reduced-Order Models in Uncertainty Quantification of Complex Turbulent Dynamical Systems

NASA Astrophysics Data System (ADS)

Qi, Di

Turbulent dynamical systems are ubiquitous in science and engineering. Uncertainty quantification (UQ) in turbulent dynamical systems is a grand challenge where the goal is to obtain statistical estimates for key physical quantities. In the development of a proper UQ scheme for systems characterized by both a high-dimensional phase space and a large number of instabilities, significant model errors compared with the true natural signal are always unavoidable due to both the imperfect understanding of the underlying physical processes and the limited computational resources available. One central issue in contemporary research is the development of a systematic methodology for reduced order models that can recover the crucial features both with model fidelity in statistical equilibrium and with model sensitivity in response to perturbations. In the first part, we discuss a general mathematical framework to construct statistically accurate reduced-order models that have skill in capturing the statistical variability in the principal directions of a general class of complex systems with quadratic nonlinearity. A systematic hierarchy of simple statistical closure schemes, which are built through new global statistical energy conservation principles combined with statistical equilibrium fidelity, are designed and tested for UQ of these problems. Second, the capacity of imperfect low-order stochastic approximations to model extreme events in a passive scalar field advected by turbulent flows is investigated. The effects in complicated flow systems are considered including strong nonlinear and non-Gaussian interactions, and much simpler and cheaper imperfect models with model error are constructed to capture the crucial statistical features in the stationary tracer field. Several mathematical ideas are introduced to improve the prediction skill of the imperfect reduced-order models. Most importantly, empirical information theory and statistical linear response theory are applied in the training phase for calibrating model errors to achieve optimal imperfect model parameters; and total statistical energy dynamics are introduced to improve the model sensitivity in the prediction phase especially when strong external perturbations are exerted. The validity of reduced-order models for predicting statistical responses and intermittency is demonstrated on a series of instructive models with increasing complexity, including the stochastic triad model, the Lorenz '96 model, and models for barotropic and baroclinic turbulence. The skillful low-order modeling methods developed here should also be useful for other applications such as efficient algorithms for data assimilation.

Prognostic significance of MCM 2 and Ki-67 in neuroblastic tumors in children.

PubMed

Lewandowska, Magdalena; Taran, Katarzyna; Sitkiewicz, Anna; Andrzejewska, Ewa

2015-12-02

Neuroblastic tumors can be characterized by three features: spontaneous regression, maturation and aggressive proliferation. The most common and routinely used method of assessing tumor cell proliferation is to determine the Ki-67 index in the tumor tissue. Despite numerous studies, neuroblastoma biology is not fully understood, which makes treatment results unsatisfactory. MCM 2 is a potential prognostic factor in the neuroblastoma group. The study is based on retrospective analysis of 35 patients treated for neuroblastic tumors in the Department of Pediatric Surgery and Oncology of the Medical University of Lodz, during the period 2001-2011. The material comprised tissues of 16 tumors excised during the operation and 19 biopsy specimens. Immunohistochemical examinations were performed with immunoperoxidase using mouse monoclonal anti-MCM 2 and anti-Ki-67 antibodies. We observed that MCM 2 expression ranged from 2% to 98% and the Ki-67 index ranged from 0 to 95%. There was a statistically significant correlation between expression of MCM 2 and the value of the Ki-67 index and a correlation close to statistical significance between expression of MCM 2 and unfavorable histopathology. There was no statistical relationship between expression of MCM 2 and age over 1 year and N-myc amplification. The presented research shows that MCM 2 may have prognostic significance in neuroblastic pediatric tumors and as a potential prognostic factor could be the starting point of new individualized therapy.

Risk management in inpatient units in the Czech Republic from the point of view of nurses in leadership positions.

PubMed

Prokešová, Radka; Brabcová, Iva; Pokojová, Radka; Bártlová, Sylva

2016-12-01

The goal of this study was to assess specific features of risk management from the point of view of nurses in leadership positions in inpatient units in Czech hospitals. The study was performed using a quantitative research strategy, i.e., a questionnaire. The data sample was analyzed using SPSS v. 23.0. Pearson's chi-square and analysis of adjusted residues were used for identifying the existence associations of nominal and/or ordinal quantities. 315 nurses in leadership positions working in inpatient units of Czech hospitals were included in the sample. The sample was created using random selection by means of quotas. Based on the study results, statistically significant relations between the respondents' education and the utilization of methods to identify risks were identified. Furthermore, statistically significant relationships were found between a nurse's functional role within the system and regular analysis and evaluation of risks and between the type of the healthcare facility and the degree of patient involvement in risk management. The study found statistically significant correlations that can be used to increase the effectiveness of risk management in inpatient units of Czech hospitals. From this perspective, the fact that patient involvement in risk management was only reported by 37.8% of respondents seems to be the most notable problem.

Computer-aided classification of Alzheimer's disease based on support vector machine with combination of cerebral image features in MRI

NASA Astrophysics Data System (ADS)

Jongkreangkrai, C.; Vichianin, Y.; Tocharoenchai, C.; Arimura, H.; Alzheimer's Disease Neuroimaging Initiative

2016-03-01

Several studies have differentiated Alzheimer's disease (AD) using cerebral image features derived from MR brain images. In this study, we were interested in combining hippocampus and amygdala volumes and entorhinal cortex thickness to improve the performance of AD differentiation. Thus, our objective was to investigate the useful features obtained from MRI for classification of AD patients using support vector machine (SVM). T1-weighted MR brain images of 100 AD patients and 100 normal subjects were processed using FreeSurfer software to measure hippocampus and amygdala volumes and entorhinal cortex thicknesses in both brain hemispheres. Relative volumes of hippocampus and amygdala were calculated to correct variation in individual head size. SVM was employed with five combinations of features (H: hippocampus relative volumes, A: amygdala relative volumes, E: entorhinal cortex thicknesses, HA: hippocampus and amygdala relative volumes and ALL: all features). Receiver operating characteristic (ROC) analysis was used to evaluate the method. AUC values of five combinations were 0.8575 (H), 0.8374 (A), 0.8422 (E), 0.8631 (HA) and 0.8906 (ALL). Although “ALL” provided the highest AUC, there were no statistically significant differences among them except for “A” feature. Our results showed that all suggested features may be feasible for computer-aided classification of AD patients.

Clinical and radiological features of invasive Klebsiella pneumoniae liver abscess syndrome.

PubMed

Shin, Sung Ui; Park, Chang Min; Lee, Youkyung; Kim, Eui-Chong; Kim, Soo Jin; Goo, Jin Mo

2013-06-01

Recently, a striking new clinical manifestation of Klebsiella pneumoniae (KP) infection referred to as invasive KP liver abscess syndrome (IKPLAS), defined by liver abscess with contemporaneous metastatic KP infections at other body sites has been documented. Until now, however, there have been relatively few reports regarding its radiologic features. To describe the clinical and radiological features of IKPLAS patients, and to compare them with those with KP liver abscess without metastatic infections to ascertain possible predictors of IKPLAS. From January 2008 to May 2010, 35 patients (26 men and 9 women; mean age, 59.4 years) with both liver abscess and metastatic KP infections were diagnosed with IKPLAS. Their clinical and radiological features were retrospectively evaluated and compared with those of 25 contemporaneous non-metastatic patients to investigate predictive factors for metastatic infections. The rate of intensive care unit admissions and overall mortality was 34.3% and 17.1% in IKPLAS patients, and was significantly higher than those of the non-metastatic group (8% and 0%, respectively). As for metastatic infections, the lung was the most common site and multiple nodules or masses (n = 9) were the most common manifestations. Univariate analysis revealed that liver abscess ≤5.8 cm, bilobar involvement of abscess and altered mentality were significantly related with IKPLAS. At multivariate analysis, liver abscess ≤5.8 cm was proven to be a significant independent predictor of IKPLAS (OR, 3.6; P = 0.038). In addition, altered mentality was present solely in IKPLAS (25.7% vs. 0%) although its P value (P = 0.052) did not reach a statistical significance at multivariate analysis. IKPLAS has significantly worse prognosis than non-metastatic KP abscess patients. In patients with KP liver abscess, liver abscess ≤5.8 cm can be used as an independent predictor of IKPLAS and altered mentality as a very specific feature in diagnosing IKPLAS. © 2013 The Foundation Acta Radiologica.

Human cytomegalovirus and Epstein-Barr virus in etiopathogenesis of apical periodontitis: a systematic review.

PubMed

Jakovljevic, Aleksandar; Andric, Miroslav

2014-01-01

During the last decade, a hypothesis has been established that human cytomegalovirus (HCMV) and Epstein-Barr virus (EBV) may be implicated in the pathogenesis of apical periodontitis. The aim of this review was to analyze the available evidence that indicates that HCMV and EBV can actually contribute to the pathogenesis of periapical lesions and to answer the following focused question: is there a relationship between HCMV and EBV DNA and/or RNA detection and the clinical features of human periapical lesions? The literature search covered MEDLINE, Science Citation Index Expanded (SCIexpanded), Scopus, and The Cochrane Library database. Quantitative statistical analysis was performed on the pooled data of HCMV and EBV messenger RNA transcripts in tissues of symptomatic and asymptomatic periapical lesions. The electronic database search yielded 48 hits from PubMed, 197 hits from Scopus, 40 hits from Web of Science, and 1 from the Cochrane Library. Seventeen cross-sectional studies have been included in the final review. The pooled results from quantitative systematic method analysis showed no statistically significant relationship between the presence of HCMV and EBV messenger RNA transcripts (P = .083 and P = .306, respectively) and the clinical features of apical periodontitis. The findings of HCMV and EBV transcripts in apical periodontitis were controversial among the included studies. Herpesviruses were common in symptomatic and large-size periapical lesions, but such results failed to reach statistical significance. Further studies, including those based on an experimental animal model, should provide more data on herpesviruses as a factor in the pathogenesis of periapical inflammation. Copyright © 2014 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

Nightside Quiet-Time Mid-Latitude Ionospheric Convection and Its Connection to Penetration Electric Fields

NASA Astrophysics Data System (ADS)

Ruohoniemi, J. M.; Maimaiti, M.; Baker, J. B.; Ribeiro, A. J.

2017-12-01

Previous studies have shown that during quiet geomagnetic conditions F-region subauroral ionospheric plasma exhibits drifts of a few tens of m/s, predominantly in the westward direction. However, the exact driving mechanisms for this plasma motion are still not well understood. Recent expansion of SuperDARN radars into the mid-latitude region has provided new opportunities to study subauroral ionospheric convection over large areas and with greater spatial resolution and statistical significance than previously possible. Mid-latitude SuperDARN radars tend to observe subauroral ionospheric backscatter with low Doppler velocities on most geomagnetically quiet nights. In this study, we have used two years of data obtained from the six mid-latitude SuperDARN radars in the North American sector to derive a statistical model of quiet-time nightside mid-latitude plasma convection between 52°- 58° magnetic latitude. The model is organized in MLAT-MLT coordinates and has a spatial resolution of 1°x 7 min with each grid cell typically counting thousands of velocity measurements. Our results show that the flow is predominantly westward (20 - 60 m/s) and weakly northward (0 -20 m/s) near midnight but with a strong seasonal dependence such that the flows tend to be strongest and most spatially variable in winter. These statistical results are in good agreement with previously reported observations from ISR measurements but also show some interesting new features, one being a significant latitudinal variation of zonal flow velocity near midnight in winter. In this presentation, we describe the derivation of the nightside quite-time subauroral convection model, analyze its most prominent features, and discuss the results in terms of the Ionosphere-Thermosphere coupling and penetration electric fields.

Cytopathologic differential diagnosis of low-grade urothelial carcinoma and reactive urothelial proliferation in bladder washings: a logistic regression analysis.

PubMed

Cakir, Ebru; Kucuk, Ulku; Pala, Emel Ebru; Sezer, Ozlem; Ekin, Rahmi Gokhan; Cakmak, Ozgur

2017-05-01

Conventional cytomorphologic assessment is the first step to establish an accurate diagnosis in urinary cytology. In cytologic preparations, the separation of low-grade urothelial carcinoma (LGUC) from reactive urothelial proliferation (RUP) can be exceedingly difficult. The bladder washing cytologies of 32 LGUC and 29 RUP were reviewed. The cytologic slides were examined for the presence or absence of the 28 cytologic features. The cytologic criteria showing statistical significance in LGUC were increased numbers of monotonous single (non-umbrella) cells, three-dimensional cellular papillary clusters without fibrovascular cores, irregular bordered clusters, atypical single cells, irregular nuclear overlap, cytoplasmic homogeneity, increased N/C ratio, pleomorphism, nuclear border irregularity, nuclear eccentricity, elongated nuclei, and hyperchromasia (p ˂ 0.05), and the cytologic criteria showing statistical significance in RUP were inflammatory background, mixture of small and large urothelial cells, loose monolayer aggregates, and vacuolated cytoplasm (p ˂ 0.05). When these variables were subjected to a stepwise logistic regression analysis, four features were selected to distinguish LGUC from RUP: increased numbers of monotonous single (non-umbrella) cells, increased nuclear cytoplasmic ratio, hyperchromasia, and presence of small and large urothelial cells (p = 0.0001). By this logistic model of the 32 cases with proven LGUC, the stepwise logistic regression analysis correctly predicted 31 (96.9%) patients with this diagnosis, and of the 29 patients with RUP, the logistic model correctly predicted 26 (89.7%) patients as having this disease. There are several cytologic features to separate LGUC from RUP. Stepwise logistic regression analysis is a valuable tool for determining the most useful cytologic criteria to distinguish these entities. © 2017 APMIS. Published by John Wiley & Sons Ltd.

Can Facebook Reduce Perceived Anxiety Among College Students? Randomized Controlled Exercise Trial Using the Transtheoretical Model of Behavior Change

PubMed Central

Frith, Emily

2017-01-01

Background Recent studies suggest social media may be an attractive strategy to promote mental health and wellness. There remains a need to examine the utility for individually tailored wellness messages posted to social media sites such as Facebook to facilitate positive psychological outcomes. Objective Our aim was to extend the growing body of evidence supporting the potential for social media to enhance mental health. We evaluated the influence of an 8-week social media intervention on anxiety in college students and examined the impact of dynamic (active) versus static (passive) Facebook content on physical activity behaviors. Methods Participants in the static group (n=21) accessed a Facebook page featuring 96 statuses. Statuses were intended to engage cognitive processes followed by behavioral processes of change per the transtheoretical model of behavior change. Content posted on the static Facebook page was identical to the dynamic page; however, the static group viewed all 96 statuses on the first day of the study, while the dynamic group received only 1 to 2 of these status updates per day throughout the intervention. Anxiety was measured using the Overall Anxiety Severity and Impairment Scale (OASIS). Time spent engaging in physical activity was assessed using the International Physical Activity Questionnaire (IPAQ). Results The OASIS change score for the dynamic Facebook group was statistically significant (P=.003), whereas the change score for the static group was not (P=.48). A statistically significant group-by-time interaction was observed (P=.03). The total IPAQ group-by-time interaction was not statistically significant (P=.06). Conclusions We observed a decrease in anxiety and increase in total physical activity for the dynamic group only. Dynamic social networking sites, featuring regularly updated content, may be more advantageous than websites that retain static content over time. Trial Registration ClinicalTrials.gov NCT03363737; https://clinicaltrials.gov/ct2/show/NCT03363737 (Archived by WebCite at http://www.webcitation.org/6vXzNbOWJ) PMID:29222077

New Optical Transforms For Statistical Image Recognition

NASA Astrophysics Data System (ADS)

Lee, Sing H.

1983-12-01

In optical implementation of statistical image recognition, new optical transforms on large images for real-time recognition are of special interest. Several important linear transformations frequently used in statistical pattern recognition have now been optically implemented, including the Karhunen-Loeve transform (KLT), the Fukunaga-Koontz transform (FKT) and the least-squares linear mapping technique (LSLMT).1-3 The KLT performs principle components analysis on one class of patterns for feature extraction. The FKT performs feature extraction for separating two classes of patterns. The LSLMT separates multiple classes of patterns by maximizing the interclass differences and minimizing the intraclass variations.

Establishing a learning foundation in a dynamically changing world: Insights from artificial language work

NASA Astrophysics Data System (ADS)

Gonzales, Kalim

It is argued that infants build a foundation for learning about the world through their incidental acquisition of the spatial and temporal regularities surrounding them. A challenge is that learning occurs across multiple contexts whose statistics can greatly differ. Two artificial language studies with 12-month-olds demonstrate that infants come prepared to parse statistics across contexts using the temporal and perceptual features that distinguish one context from another. These results suggest that infants can organize their statistical input with a wider range of features that typically considered. Possible attention, decision making, and memory mechanisms are discussed.

Radiomic analysis in prediction of Human Papilloma Virus status.

PubMed

Yu, Kaixian; Zhang, Youyi; Yu, Yang; Huang, Chao; Liu, Rongjie; Li, Tengfei; Yang, Liuqing; Morris, Jeffrey S; Baladandayuthapani, Veerabhadran; Zhu, Hongtu

2017-12-01

Human Papilloma Virus (HPV) has been associated with oropharyngeal cancer prognosis. Traditionally the HPV status is tested through invasive lab test. Recently, the rapid development of statistical image analysis techniques has enabled precise quantitative analysis of medical images. The quantitative analysis of Computed Tomography (CT) provides a non-invasive way to assess HPV status for oropharynx cancer patients. We designed a statistical radiomics approach analyzing CT images to predict HPV status. Various radiomics features were extracted from CT scans, and analyzed using statistical feature selection and prediction methods. Our approach ranked the highest in the 2016 Medical Image Computing and Computer Assisted Intervention (MICCAI) grand challenge: Oropharynx Cancer (OPC) Radiomics Challenge, Human Papilloma Virus (HPV) Status Prediction. Further analysis on the most relevant radiomic features distinguishing HPV positive and negative subjects suggested that HPV positive patients usually have smaller and simpler tumors.

Administrative records and surveys as basis for statistics on international labour migration.

PubMed

Hoffmann, E

1997-08-01

"This paper discusses possible sources for statistics to be used for describing and analysing the number, structure, situation, development and impact of migrant workers. The discussion is focused on key, intrinsic features of the different sources, important for the understanding of their strengths and weaknesses, and draws the reader's attention to features which may tend to undermine the quality of statistics produced as well as ways in which the impact of such features can be evaluated and, if possible, reduced.... The paper is organized around three key groups of migrant workers: (a) Persons who are arriving in a country to work there, i.e. the inflow of foreign workers; (b) Persons who are leaving their country to find work abroad, i.e. the outflow of migrant workers; [and] (c) Stock of foreign workers in the country." (EXCERPT)

Detection of reflecting surfaces by a statistical model

NASA Astrophysics Data System (ADS)

He, Qiang; Chu, Chee-Hung H.

2009-02-01

Remote sensing is widely used assess the destruction from natural disasters and to plan relief and recovery operations. How to automatically extract useful features and segment interesting objects from digital images, including remote sensing imagery, becomes a critical task for image understanding. Unfortunately, current research on automated feature extraction is ignorant of contextual information. As a result, the fidelity of populating attributes corresponding to interesting features and objects cannot be satisfied. In this paper, we present an exploration on meaningful object extraction integrating reflecting surfaces. Detection of specular reflecting surfaces can be useful in target identification and then can be applied to environmental monitoring, disaster prediction and analysis, military, and counter-terrorism. Our method is based on a statistical model to capture the statistical properties of specular reflecting surfaces. And then the reflecting surfaces are detected through cluster analysis.

A bootstrap based Neyman-Pearson test for identifying variable importance.

PubMed

Ditzler, Gregory; Polikar, Robi; Rosen, Gail

2015-04-01

Selection of most informative features that leads to a small loss on future data are arguably one of the most important steps in classification, data analysis and model selection. Several feature selection (FS) algorithms are available; however, due to noise present in any data set, FS algorithms are typically accompanied by an appropriate cross-validation scheme. In this brief, we propose a statistical hypothesis test derived from the Neyman-Pearson lemma for determining if a feature is statistically relevant. The proposed approach can be applied as a wrapper to any FS algorithm, regardless of the FS criteria used by that algorithm, to determine whether a feature belongs in the relevant set. Perhaps more importantly, this procedure efficiently determines the number of relevant features given an initial starting point. We provide freely available software implementations of the proposed methodology.

Kansas environmental and resource study: A Great Plains model, tasks 1-6

NASA Technical Reports Server (NTRS)

Haralick, R. M.; Kanemasu, E. T.; Morain, S. A.; Yarger, H. L. (Principal Investigator); Ulaby, F. T.; Shanmugam, K. S.; Williams, D. L.; Mccauley, J. R.; Mcnaughton, J. L.

1972-01-01

There are no author identified significant results in this report. Environmental and resources investigations in Kansas utilizing ERTS-1 imagery are summarized for the following areas: (1) use of feature extraction techniqued for texture context information in ERTS imagery; (2) interpretation and automatic image enhancement; (3) water use, production, and disease detection and predictions for wheat; (4) ERTS-1 agricultural statistics; (5) monitoring fresh water resources; and (6) ground pattern analysis in the Great Plains.

Identifying Epigenetic Biomarkers using Maximal Relevance and Minimal Redundancy Based Feature Selection for Multi-Omics Data.

PubMed

Mallik, Saurav; Bhadra, Tapas; Maulik, Ujjwal

2017-01-01

Epigenetic Biomarker discovery is an important task in bioinformatics. In this article, we develop a new framework of identifying statistically significant epigenetic biomarkers using maximal-relevance and minimal-redundancy criterion based feature (gene) selection for multi-omics dataset. Firstly, we determine the genes that have both expression as well as methylation values, and follow normal distribution. Similarly, we identify the genes which consist of both expression and methylation values, but do not follow normal distribution. For each case, we utilize a gene-selection method that provides maximal-relevant, but variable-weighted minimum-redundant genes as top ranked genes. For statistical validation, we apply t-test on both the expression and methylation data consisting of only the normally distributed top ranked genes to determine how many of them are both differentially expressed andmethylated. Similarly, we utilize Limma package for performing non-parametric Empirical Bayes test on both expression and methylation data comprising only the non-normally distributed top ranked genes to identify how many of them are both differentially expressed and methylated. We finally report the top-ranking significant gene-markerswith biological validation. Moreover, our framework improves positive predictive rate and reduces false positive rate in marker identification. In addition, we provide a comparative analysis of our gene-selection method as well as othermethods based on classificationperformances obtained using several well-known classifiers.

Statistics of galaxy orientations - Morphology and large-scale structure

NASA Technical Reports Server (NTRS)

Lambas, Diego G.; Groth, Edward J.; Peebles, P. J. E.

1988-01-01

Using the Uppsala General Catalog of bright galaxies and the northern and southern maps of the Lick counts of galaxies, statistical evidence of a morphology-orientation effect is found. Major axes of elliptical galaxies are preferentially oriented along the large-scale features of the Lick maps. However, the orientations of the major axes of spiral and lenticular galaxies show no clear signs of significant nonrandom behavior at a level of less than about one-fifth of the effect seen for ellipticals. The angular scale of the detected alignment effect for Uppsala ellipticals extends to at least theta of about 2 deg, which at a redshift of z of about 0.02 corresponds to a linear scale of about 2/h Mpc.

On-line transmission electron microscopic image analysis of chromatin texture for differentiation of thyroid gland tumors.

PubMed

Kriete, A; Schäffer, R; Harms, H; Aus, H M

1987-06-01

Nuclei of the cells from the thyroid gland were analyzed in a transmission electron microscope by direct TV scanning and on-line image processing. The method uses the advantages of a visual-perception model to detect structures in noisy and low-contrast images. The features analyzed include area, a form factor and texture parameters from the second derivative stage. Three tumor-free thyroid tissues, three follicular adenomas, three follicular carcinomas and three papillary carcinomas were studied. The computer-aided cytophotometric method showed that the most significant differences were the statistics of the chromatin texture features of homogeneity and regularity. These findings document the possibility of an automated differentiation of tumors at the ultrastructural level.

Visual feature extraction from voxel-weighted averaging of stimulus images in 2 fMRI studies.

PubMed

Hart, Corey B; Rose, William J

2013-11-01

Multiple studies have provided evidence for distributed object representation in the brain, with several recent experiments leveraging basis function estimates for partial image reconstruction from fMRI data. Using a novel combination of statistical decomposition, generalized linear models, and stimulus averaging on previously examined image sets and Bayesian regression of recorded fMRI activity during presentation of these data sets, we identify a subset of relevant voxels that appear to code for covarying object features. Using a technique we term "voxel-weighted averaging," we isolate image filters that these voxels appear to implement. The results, though very cursory, appear to have significant implications for hierarchical and deep-learning-type approaches toward the understanding of neural coding and representation.

Extraction of multi-scale landslide morphological features based on local Gi* using airborne LiDAR-derived DEM

NASA Astrophysics Data System (ADS)

Shi, Wenzhong; Deng, Susu; Xu, Wenbing

2018-02-01

For automatic landslide detection, landslide morphological features should be quantitatively expressed and extracted. High-resolution Digital Elevation Models (DEMs) derived from airborne Light Detection and Ranging (LiDAR) data allow fine-scale morphological features to be extracted, but noise in DEMs influences morphological feature extraction, and the multi-scale nature of landslide features should be considered. This paper proposes a method to extract landslide morphological features characterized by homogeneous spatial patterns. Both profile and tangential curvature are utilized to quantify land surface morphology, and a local Gi* statistic is calculated for each cell to identify significant patterns of clustering of similar morphometric values. The method was tested on both synthetic surfaces simulating natural terrain and airborne LiDAR data acquired over an area dominated by shallow debris slides and flows. The test results of the synthetic data indicate that the concave and convex morphologies of the simulated terrain features at different scales and distinctness could be recognized using the proposed method, even when random noise was added to the synthetic data. In the test area, cells with large local Gi* values were extracted at a specified significance level from the profile and the tangential curvature image generated from the LiDAR-derived 1-m DEM. The morphologies of landslide main scarps, source areas and trails were clearly indicated, and the morphological features were represented by clusters of extracted cells. A comparison with the morphological feature extraction method based on curvature thresholds proved the proposed method's robustness to DEM noise. When verified against a landslide inventory, the morphological features of almost all recent (< 5 years) landslides and approximately 35% of historical (> 10 years) landslides were extracted. This finding indicates that the proposed method can facilitate landslide detection, although the cell clusters extracted from curvature images should be filtered using a filtering strategy based on supplementary information provided by expert knowledge or other data sources.

Morphologic Features of Magnetic Resonance Imaging as a Surrogate of Capsular Contracture in Breast Cancer Patients With Implant-based Reconstructions.

PubMed

Tyagi, Neelam; Sutton, Elizabeth; Hunt, Margie; Zhang, Jing; Oh, Jung Hun; Apte, Aditya; Mechalakos, James; Wilgucki, Molly; Gelb, Emily; Mehrara, Babak; Matros, Evan; Ho, Alice

2017-02-01

Capsular contracture (CC) is a serious complication in patients receiving implant-based reconstruction for breast cancer. Currently, no objective methods are available for assessing CC. The goal of the present study was to identify image-based surrogates of CC using magnetic resonance imaging (MRI). We analyzed a retrospective data set of 50 patients who had undergone both a diagnostic MRI scan and a plastic surgeon's evaluation of the CC score (Baker's score) within a 6-month period after mastectomy and reconstructive surgery. The MRI scans were assessed for morphologic shape features of the implant and histogram features of the pectoralis muscle. The shape features, such as roundness, eccentricity, solidity, extent, and ratio length for the implant, were compared with the Baker score. For the pectoralis muscle, the muscle width and median, skewness, and kurtosis of the intensity were compared with the Baker score. Univariate analysis (UVA) using a Wilcoxon rank-sum test and multivariate analysis with the least absolute shrinkage and selection operator logistic regression was performed to determine significant differences in these features between the patient groups categorized according to their Baker's scores. UVA showed statistically significant differences between grade 1 and grade ≥2 for morphologic shape features and histogram features, except for volume and skewness. Only eccentricity, ratio length, and volume were borderline significant in differentiating grade ≤2 and grade ≥3. Features with P<.1 on UVA were used in the multivariate least absolute shrinkage and selection operator logistic regression analysis. Multivariate analysis showed a good level of predictive power for grade 1 versus grade ≥2 CC (area under the receiver operating characteristic curve 0.78, sensitivity 0.78, and specificity 0.82) and for grade ≤2 versus grade ≥3 CC (area under the receiver operating characteristic curve 0.75, sensitivity 0.75, and specificity 0.79). The morphologic shape features described on MR images were associated with the severity of CC. MRI has the potential to further improve the diagnostic ability of the Baker score in breast cancer patients who undergo implant reconstruction. Copyright © 2016 Elsevier Inc. All rights reserved.

Fluorescence and fluorescence-lifetime imaging microscopy (FLIM) to characterize yeast strains by autofluorescence

NASA Astrophysics Data System (ADS)

Bhatta, H.; Goldys, E. M.; Ma, J.

2006-02-01

We characterised populations of wild type baking and brewing yeast cells using intrinsic fluorescence and fluorescence lifetime microscopy, in order to obtain quantitative identifiers of different strains. The cell autofluorescence was excited at 405 nm and observed within 440-540 nm range where strong cell to cell variability was observed. The images were analyzed using customised public domain software, which provided information on cell size, intensity and texture-related features. In light of significant diversity of the data, statistical methods were utilized to assess the validity of the proposed quantitative identifiers for strain differentiation. The Kolmogorov-Smirnov test was applied to confirm that empirical distribution functions for size, intensity and entropy for different strains were statistically different. These characteristics were followed with culture age of 24, 48 and 72 h, (the latter corresponding to a stationary growth phase) and size, and to some extent entropy, were found to be independent of age. The fluorescence intensity presented a distinctive evolution with age, different for each of the examined strains. The lifetime analysis revealed a short decay time component of 1.4 ns and a second, longer one with the average value of 3.5 ns and a broad distribution. High variability of lifetime values within cells was observed however a lifetime texture feature in the studied strains was statistically different.

Personalised news filtering and recommendation system using Chi-square statistics-based K-nearest neighbour (χ2SB-KNN) model

NASA Astrophysics Data System (ADS)

Adeniyi, D. A.; Wei, Z.; Yang, Y.

2017-10-01

Recommendation problem has been extensively studied by researchers in the field of data mining, database and information retrieval. This study presents the design and realisation of an automated, personalised news recommendations system based on Chi-square statistics-based K-nearest neighbour (χ2SB-KNN) model. The proposed χ2SB-KNN model has the potential to overcome computational complexity and information overloading problems, reduces runtime and speeds up execution process through the use of critical value of χ2 distribution. The proposed recommendation engine can alleviate scalability challenges through combined online pattern discovery and pattern matching for real-time recommendations. This work also showcases the development of a novel method of feature selection referred to as Data Discretisation-Based feature selection method. This is used for selecting the best features for the proposed χ2SB-KNN algorithm at the preprocessing stage of the classification procedures. The implementation of the proposed χ2SB-KNN model is achieved through the use of a developed in-house Java program on an experimental website called OUC newsreaders' website. Finally, we compared the performance of our system with two baseline methods which are traditional Euclidean distance K-nearest neighbour and Naive Bayesian techniques. The result shows a significant improvement of our method over the baseline methods studied.

Photoanthropometric Study of Dysmorphic Features of the Face in Children with Autism and Asperger Syndrome

PubMed Central

Kapinos- Gorczyca, Agnieszka; Ziora, Katarzyna; Oświęcimska, Joanna

2012-01-01

Objective Childhood autism is a neurodevelopmental disorder characterized by impairments in social interactions, verbal and non-verbal communication and by a pattern of stereotypical behaviors and interests. The aim of this study was to estimate the dysmorphic facial features of children with autism and children with Asperger syndrome. Methods The examination was conducted on 60 children (30 with childhood autism and 30 with Asperger syndrome). The photo anthropometric method used in this study followed the protocol established by Stengel-Rutkowski et al. Results The performed statistical analysis showed that in patients with childhood autism, the anteriorly rotated ears and the long back of the nose appeared more often. In the group of children with autism, there was a connection between the amount of dysmorphies and the presence of some somatic diseases in the first-degree relatives. There was also a connection between the motor coordination and the age the child began to walk. Discussion In patients with childhood autism, there were certain dysmorphies (like the anterior rotated ears and the long back of the nose) which appeared more often. Although the connection was not statistically significant, it seemed to concur with data from the literature. Conclusion Formulation of the other conclusions would require broader studies e.g. dealing with a familial analysis of dysmorphic features. PMID:23056117

A genome-wide methylation study on obesity: differential variability and differential methylation.

PubMed

Xu, Xiaojing; Su, Shaoyong; Barnes, Vernon A; De Miguel, Carmen; Pollock, Jennifer; Ownby, Dennis; Shi, Hidong; Zhu, Haidong; Snieder, Harold; Wang, Xiaoling

2013-05-01

Besides differential methylation, DNA methylation variation has recently been proposed and demonstrated to be a potential contributing factor to cancer risk. Here we aim to examine whether differential variability in methylation is also an important feature of obesity, a typical non-malignant common complex disease. We analyzed genome-wide methylation profiles of over 470,000 CpGs in peripheral blood samples from 48 obese and 48 lean African-American youth aged 14-20 y old. A substantial number of differentially variable CpG sites (DVCs), using statistics based on variances, as well as a substantial number of differentially methylated CpG sites (DMCs), using statistics based on means, were identified. Similar to the findings in cancers, DVCs generally exhibited an outlier structure and were more variable in cases than in controls. By randomly splitting the current sample into a discovery and validation set, we observed that both the DVCs and DMCs identified from the first set could independently predict obesity status in the second set. Furthermore, both the genes harboring DMCs and the genes harboring DVCs showed significant enrichment of genes identified by genome-wide association studies on obesity and related diseases, such as hypertension, dyslipidemia, type 2 diabetes and certain types of cancers, supporting their roles in the etiology and pathogenesis of obesity. We generalized the recent finding on methylation variability in cancer research to obesity and demonstrated that differential variability is also an important feature of obesity-related methylation changes. Future studies on the epigenetics of obesity will benefit from both statistics based on means and statistics based on variances.

Statistical properties of Chinese phonemic networks

NASA Astrophysics Data System (ADS)

Yu, Shuiyuan; Liu, Haitao; Xu, Chunshan

2011-04-01

The study of properties of speech sound systems is of great significance in understanding the human cognitive mechanism and the working principles of speech sound systems. Some properties of speech sound systems, such as the listener-oriented feature and the talker-oriented feature, have been unveiled with the statistical study of phonemes in human languages and the research of the interrelations between human articulatory gestures and the corresponding acoustic parameters. With all the phonemes of speech sound systems treated as a coherent whole, our research, which focuses on the dynamic properties of speech sound systems in operation, investigates some statistical parameters of Chinese phoneme networks based on real text and dictionaries. The findings are as follows: phonemic networks have high connectivity degrees and short average distances; the degrees obey normal distribution and the weighted degrees obey power law distribution; vowels enjoy higher priority than consonants in the actual operation of speech sound systems; the phonemic networks have high robustness against targeted attacks and random errors. In addition, for investigating the structural properties of a speech sound system, a statistical study of dictionaries is conducted, which shows the higher frequency of shorter words and syllables and the tendency that the longer a word is, the shorter the syllables composing it are. From these structural properties and dynamic properties one can derive the following conclusion: the static structure of a speech sound system tends to promote communication efficiency and save articulation effort while the dynamic operation of this system gives preference to reliable transmission and easy recognition. In short, a speech sound system is an effective, efficient and reliable communication system optimized in many aspects.

Dream features in the early stages of Parkinson's disease.

PubMed

Bugalho, Paulo; Paiva, Teresa

2011-11-01

Few studies have investigated the relation between dream features and cognition in Parkinson's disease (PD), although vivid dreams, hallucinations and cognitive decline have been proposed as successive steps of a pathological continuum. Our objectives were therefore to characterize the dreams of early stage PD and to study the relation between dream characteristics, cognitive function, motor status, depression, dopaminergic treatment, and the presence of REM sleep behaviour disorder (RBD) and hallucinations. Dreams of 19 male PD patients and 21 matched control subjects were classified according to Hall and van de Castle system. h statistics was used to compare the dream content between patients and controls. We tested the relation between patients' dreams characteristics and cognitive function (Frontal assessment battery (FAB) and Mini-Mental State Examination tests) depression (Beck depression inventory), motor function (UPDRS), dopaminergic treatment, the presence of RBD (according to clinical criteria) and hallucinations, using general linear model statistics. Patients and controls differed only on FAB scores. Relevant differences in the Hall and van de Castle scale were found between patient's dreams and those of the control group, regarding animals, aggression/friendliness, physical aggression, befriender (higher in the patient group) and aggressor and bodily misfortunes (lower in the patient group) features. Cognitive and particularly frontal dysfunction had a significant influence on the frequency of physical aggression and animal related features, while dopaminergic doses, depressive symptoms, hallucinations and RBD did not. We found a pattern of dream alteration characterized by heightened aggressiveness and the presence of animals. These were related to more severe frontal dysfunction, which could be the origin of such changes.

Structural damage detection based on stochastic subspace identification and statistical pattern recognition: I. Theory

NASA Astrophysics Data System (ADS)

Ren, W. X.; Lin, Y. Q.; Fang, S. E.

2011-11-01

One of the key issues in vibration-based structural health monitoring is to extract the damage-sensitive but environment-insensitive features from sampled dynamic response measurements and to carry out the statistical analysis of these features for structural damage detection. A new damage feature is proposed in this paper by using the system matrices of the forward innovation model based on the covariance-driven stochastic subspace identification of a vibrating system. To overcome the variations of the system matrices, a non-singularity transposition matrix is introduced so that the system matrices are normalized to their standard forms. For reducing the effects of modeling errors, noise and environmental variations on measured structural responses, a statistical pattern recognition paradigm is incorporated into the proposed method. The Mahalanobis and Euclidean distance decision functions of the damage feature vector are adopted by defining a statistics-based damage index. The proposed structural damage detection method is verified against one numerical signal and two numerical beams. It is demonstrated that the proposed statistics-based damage index is sensitive to damage and shows some robustness to the noise and false estimation of the system ranks. The method is capable of locating damage of the beam structures under different types of excitations. The robustness of the proposed damage detection method to the variations in environmental temperature is further validated in a companion paper by a reinforced concrete beam tested in the laboratory and a full-scale arch bridge tested in the field.

Statistical universals reveal the structures and functions of human music.

PubMed

Savage, Patrick E; Brown, Steven; Sakai, Emi; Currie, Thomas E

2015-07-21

Music has been called "the universal language of mankind." Although contemporary theories of music evolution often invoke various musical universals, the existence of such universals has been disputed for decades and has never been empirically demonstrated. Here we combine a music-classification scheme with statistical analyses, including phylogenetic comparative methods, to examine a well-sampled global set of 304 music recordings. Our analyses reveal no absolute universals but strong support for many statistical universals that are consistent across all nine geographic regions sampled. These universals include 18 musical features that are common individually as well as a network of 10 features that are commonly associated with one another. They span not only features related to pitch and rhythm that are often cited as putative universals but also rarely cited domains including performance style and social context. These cross-cultural structural regularities of human music may relate to roles in facilitating group coordination and cohesion, as exemplified by the universal tendency to sing, play percussion instruments, and dance to simple, repetitive music in groups. Our findings highlight the need for scientists studying music evolution to expand the range of musical cultures and musical features under consideration. The statistical universals we identified represent important candidates for future investigation.

Statistical universals reveal the structures and functions of human music

PubMed Central

Savage, Patrick E.; Brown, Steven; Sakai, Emi; Currie, Thomas E.

2015-01-01

Music has been called “the universal language of mankind.” Although contemporary theories of music evolution often invoke various musical universals, the existence of such universals has been disputed for decades and has never been empirically demonstrated. Here we combine a music-classification scheme with statistical analyses, including phylogenetic comparative methods, to examine a well-sampled global set of 304 music recordings. Our analyses reveal no absolute universals but strong support for many statistical universals that are consistent across all nine geographic regions sampled. These universals include 18 musical features that are common individually as well as a network of 10 features that are commonly associated with one another. They span not only features related to pitch and rhythm that are often cited as putative universals but also rarely cited domains including performance style and social context. These cross-cultural structural regularities of human music may relate to roles in facilitating group coordination and cohesion, as exemplified by the universal tendency to sing, play percussion instruments, and dance to simple, repetitive music in groups. Our findings highlight the need for scientists studying music evolution to expand the range of musical cultures and musical features under consideration. The statistical universals we identified represent important candidates for future investigation. PMID:26124105

Towards accurate modelling of galaxy clustering on small scales: testing the standard ΛCDM + halo model

NASA Astrophysics Data System (ADS)

Sinha, Manodeep; Berlind, Andreas A.; McBride, Cameron K.; Scoccimarro, Roman; Piscionere, Jennifer A.; Wibking, Benjamin D.

2018-07-01

Interpreting the small-scale clustering of galaxies with halo models can elucidate the connection between galaxies and dark matter haloes. Unfortunately, the modelling is typically not sufficiently accurate for ruling out models statistically. It is thus difficult to use the information encoded in small scales to test cosmological models or probe subtle features of the galaxy-halo connection. In this paper, we attempt to push halo modelling into the `accurate' regime with a fully numerical mock-based methodology and careful treatment of statistical and systematic errors. With our forward-modelling approach, we can incorporate clustering statistics beyond the traditional two-point statistics. We use this modelling methodology to test the standard Λ cold dark matter (ΛCDM) + halo model against the clustering of Sloan Digital Sky Survey (SDSS) seventh data release (DR7) galaxies. Specifically, we use the projected correlation function, group multiplicity function, and galaxy number density as constraints. We find that while the model fits each statistic separately, it struggles to fit them simultaneously. Adding group statistics leads to a more stringent test of the model and significantly tighter constraints on model parameters. We explore the impact of varying the adopted halo definition and cosmological model and find that changing the cosmology makes a significant difference. The most successful model we tried (Planck cosmology with Mvir haloes) matches the clustering of low-luminosity galaxies, but exhibits a 2.3σ tension with the clustering of luminous galaxies, thus providing evidence that the `standard' halo model needs to be extended. This work opens the door to adding interesting freedom to the halo model and including additional clustering statistics as constraints.

Use of Nintendo Wii Balance Board for posturographic analysis of Multiple Sclerosis patients with minimal balance impairment.

PubMed

Severini, Giacomo; Straudi, Sofia; Pavarelli, Claudia; Da Roit, Marco; Martinuzzi, Carlotta; Di Marco Pizzongolo, Laura; Basaglia, Nino

2017-03-11

The Wii Balance Board (WBB) has been proposed as an inexpensive alternative to laboratory-grade Force Plates (FP) for the instrumented assessment of balance. Previous studies have reported a good validity and reliability of the WBB for estimating the path length of the Center of Pressure. Here we extend this analysis to 18 balance related features extracted from healthy subjects (HS) and individuals affected by Multiple Sclerosis (MS) with minimal balance impairment. Eighteen MS patients with minimal balance impairment (Berg Balance Scale 53.3 ± 3.1) and 18 age-matched HS were recruited in this study. All subjects underwent instrumented balance tests on the FP and WBB consisting of quiet standing with the eyes open and closed. Linear correlation analysis and Bland-Altman plots were used to assess relations between path lengths estimated using the WBB and the FP. 18 features were extracted from the instrumented balance tests. Statistical analysis was used to assess significant differences between the features estimated using the WBB and the FP and between HS and MS. The Spearman correlation coefficient was used to evaluate the validity and the Intraclass Correlation Coefficient was used to assess the reliability of WBB measures with respect to the FP. Classifiers based on Support Vector Machines trained on the FP and WBB features were used to assess the ability of both devices to discriminate between HS and MS. We found a significant linear relation between the path lengths calculated from the WBB and the FP indicating an overestimation of these parameters in the WBB. We observed significant differences in the path lengths between FP and WBB in most conditions. However, significant differences were not found for the majority of the other features. We observed the same significant differences between the HS and MS populations across the two measurement systems. Validity and reliability were moderate-to-high for all the analyzed features. Both the FP and WBB trained classifier showed similar classification performance (>80%) when discriminating between HS and MS. Our results support the observation that the WBB, although not suitable for obtaining absolute measures, could be successfully used in comparative analysis of different populations.

A novel examination of atypical major depressive disorder based on attachment theory.

PubMed

Levitan, Robert D; Atkinson, Leslie; Pedersen, Rebecca; Buis, Tom; Kennedy, Sidney H; Chopra, Kevin; Leung, Eman M; Segal, Zindel V

2009-06-01

While a large body of descriptive work has thoroughly investigated the clinical correlates of atypical depression, little is known about its fundamental origins. This study examined atypical depression from an attachment theory framework. Our hypothesis was that, compared to adults with melancholic depression, those with atypical depression would report more anxious-ambivalent attachment and less secure attachment. As gender has been an important consideration in prior work on atypical depression, this same hypothesis was further tested in female subjects only. One hundred ninety-nine consecutive adults presenting to a tertiary mood disorders clinic with major depressive disorder with either atypical or melancholic features according to the Structured Clinical Interview for DSM-IV Axis-I Disorders were administered a self-report adult attachment questionnaire to assess the core dimensions of secure, anxious-ambivalent, and avoidant attachment. Attachment scores were compared across the 2 depressed groups defined by atypical and melancholic features using multivariate analysis of variance. The study was conducted between 1999 and 2004. When men and women were considered together, the multivariate test comparing attachment scores by depressive group was statistically significant at p < .05. Between-subjects testing indicated that atypical depression was associated with significantly lower secure attachment scores, with a trend toward higher anxious-ambivalent attachment scores, than was melancholia. When women were analyzed separately, the multivariate test was statistically significant at p < .01, with both secure and anxious-ambivalent attachment scores differing significantly across depressive groups. These preliminary findings suggest that attachment theory, and insecure and anxious-ambivalent attachment in particular, may be a useful framework from which to study the origins, clinical correlates, and treatment of atypical depression. Gender may be an important consideration when considering atypical depression from an attachment perspective. Copyright 2009 Physicians Postgraduate Press, Inc.

Liquid-based cytology improves preoperative diagnostic accuracy of the tall cell variant of papillary thyroid carcinoma.

PubMed

Lee, Sung Hak; Jung, Chan Kwon; Bae, Ja Seong; Jung, So Lyung; Choi, Yeong Jin; Kang, Chang Suk

2014-01-01

The tall cell variant (TCV) of papillary thyroid carcinoma (PTC) is the most common among the aggressive variants of the disease. We aimed to investigate the clinicopathologic characteristics of TCV, and evaluate the diagnostic efficacy of liquid-based cytology (LBC) in TCV detection compared with conventional smear in thyroid fine needle aspiration (FNA). A total of 266 consecutive patients (220 women and 46 men) with PTC were enrolled. We analyzed tumor characteristics according to histologic growth patterns as classic, classic PTC with tall cell features, and TCV. The cytomorphologic features of these subtypes were investigated according to the preparation methods of conventional smear and LBC. TCV and classic PTC with tall cell features comprised 4.9% and 6.0% of all tumors, respectively, and were significantly associated with older age at presentation, larger tumor size, high frequency of extrathyroid extension, and BRAF mutation in comparison with classic PTC. However, there was no statistically significant difference in clinicopathologic features between TCV and classic PTC with tall cell features. Tall cells were more easily detected by LBC than by conventional smear. The percentage of tall cells identified using LBC was well correlated with three histologic subtypes. Our results demonstrate that TCV is more common than previously recognized in Korea and any PTC containing tall cells may have identical biological behavior regardless of the precise proportions of tall cells. It is possible to make a preoperative diagnosis of TCV using LBC. Copyright © 2013 Wiley Periodicals, Inc.

Clustering-based Feature Learning on Variable Stars

NASA Astrophysics Data System (ADS)

Mackenzie, Cristóbal; Pichara, Karim; Protopapas, Pavlos

2016-04-01

The success of automatic classification of variable stars depends strongly on the lightcurve representation. Usually, lightcurves are represented as a vector of many descriptors designed by astronomers called features. These descriptors are expensive in terms of computing, require substantial research effort to develop, and do not guarantee a good classification. Today, lightcurve representation is not entirely automatic; algorithms must be designed and manually tuned up for every survey. The amounts of data that will be generated in the future mean astronomers must develop scalable and automated analysis pipelines. In this work we present a feature learning algorithm designed for variable objects. Our method works by extracting a large number of lightcurve subsequences from a given set, which are then clustered to find common local patterns in the time series. Representatives of these common patterns are then used to transform lightcurves of a labeled set into a new representation that can be used to train a classifier. The proposed algorithm learns the features from both labeled and unlabeled lightcurves, overcoming the bias using only labeled data. We test our method on data sets from the Massive Compact Halo Object survey and the Optical Gravitational Lensing Experiment; the results show that our classification performance is as good as and in some cases better than the performance achieved using traditional statistical features, while the computational cost is significantly lower. With these promising results, we believe that our method constitutes a significant step toward the automation of the lightcurve classification pipeline.

Quantitative Ultrasound Using Texture Analysis of Myofascial Pain Syndrome in the Trapezius.

PubMed

Kumbhare, Dinesh A; Ahmed, Sara; Behr, Michael G; Noseworthy, Michael D

2018-01-01

Objective-The objective of this study is to assess the discriminative ability of textural analyses to assist in the differentiation of the myofascial trigger point (MTrP) region from normal regions of skeletal muscle. Also, to measure the ability to reliably differentiate between three clinically relevant groups: healthy asymptomatic, latent MTrPs, and active MTrP. Methods-18 and 19 patients were identified with having active and latent MTrPs in the trapezius muscle, respectively. We included 24 healthy volunteers. Images were obtained by research personnel, who were blinded with respect to the clinical status of the study participant. Histograms provided first-order parameters associated with image grayscale. Haralick, Galloway, and histogram-related features were used in texture analysis. Blob analysis was conducted on the regions of interest (ROIs). Principal component analysis (PCA) was performed followed by multivariate analysis of variance (MANOVA) to determine the statistical significance of the features. Results-92 texture features were analyzed for factorability using Bartlett's test of sphericity, which was significant. The Kaiser-Meyer-Olkin measure of sampling adequacy was 0.94. PCA demonstrated rotated eigenvalues of the first eight components (each comprised of multiple texture features) explained 94.92% of the cumulative variance in the ultrasound image characteristics. The 24 features identified by PCA were included in the MANOVA as dependent variables, and the presence of a latent or active MTrP or healthy muscle were independent variables. Conclusion-Texture analysis techniques can discriminate between the three clinically relevant groups.

Low Frequency Variants, Collapsed Based on Biological Knowledge, Uncover Complexity of Population Stratification in 1000 Genomes Project Data

PubMed Central

Moore, Carrie B.; Wallace, John R.; Wolfe, Daniel J.; Frase, Alex T.; Pendergrass, Sarah A.; Weiss, Kenneth M.; Ritchie, Marylyn D.

2013-01-01

Analyses investigating low frequency variants have the potential for explaining additional genetic heritability of many complex human traits. However, the natural frequencies of rare variation between human populations strongly confound genetic analyses. We have applied a novel collapsing method to identify biological features with low frequency variant burden differences in thirteen populations sequenced by the 1000 Genomes Project. Our flexible collapsing tool utilizes expert biological knowledge from multiple publicly available database sources to direct feature selection. Variants were collapsed according to genetically driven features, such as evolutionary conserved regions, regulatory regions genes, and pathways. We have conducted an extensive comparison of low frequency variant burden differences (MAF<0.03) between populations from 1000 Genomes Project Phase I data. We found that on average 26.87% of gene bins, 35.47% of intergenic bins, 42.85% of pathway bins, 14.86% of ORegAnno regulatory bins, and 5.97% of evolutionary conserved regions show statistically significant differences in low frequency variant burden across populations from the 1000 Genomes Project. The proportion of bins with significant differences in low frequency burden depends on the ancestral similarity of the two populations compared and types of features tested. Even closely related populations had notable differences in low frequency burden, but fewer differences than populations from different continents. Furthermore, conserved or functionally relevant regions had fewer significant differences in low frequency burden than regions under less evolutionary constraint. This degree of low frequency variant differentiation across diverse populations and feature elements highlights the critical importance of considering population stratification in the new era of DNA sequencing and low frequency variant genomic analyses. PMID:24385916

Textural characterization of histopathological images for oral sub-mucous fibrosis detection.

PubMed

Krishnan, M Muthu Rama; Shah, Pratik; Choudhary, Anirudh; Chakraborty, Chandan; Paul, Ranjan Rashmi; Ray, Ajoy K

2011-10-01

In the field of quantitative microscopy, textural information plays a significant role very often in tissue characterization and diagnosis, in addition to morphology and intensity. The aim of this work is to improve the classification accuracy based on textural features for the development of a computer assisted screening of oral sub-mucous fibrosis (OSF). In fact, a systematic approach is introduced in order to grade the histopathological tissue sections into normal, OSF without dysplasia and OSF with dysplasia, which would help the oral onco-pathologists to screen the subjects rapidly. In totality, 71 textural features are extracted from epithelial region of the tissue sections using various wavelet families, Gabor-wavelet, local binary pattern, fractal dimension and Brownian motion curve, followed by preprocessing and segmentation. Wavelet families contribute a common set of 9 features, out of which 8 are significant and other 61 out of 62 obtained from the rest of the extractors are also statistically significant (p<0.05) in discriminating the three stages. Based on mean distance criteria, the best wavelet family (i.e., biorthogonal3.1 (bior3.1)) is selected for classifier design. support vector machine (SVM) is trained by 146 samples based on 69 textural features and its classification accuracy is computed for each of the combinations of wavelet family and rest of the extractors. Finally, it has been investigated that bior3.1 wavelet coefficients leads to higher accuracy (88.38%) in combination with LBP and Gabor wavelet features through three-fold cross validation. Results are shown and discussed in detail. It is shown that combining more than one texture measure instead of using just one might improve the overall accuracy. Copyright © 2011 Elsevier Ltd. All rights reserved.

Insights into Corona Formation through Statistical Analyses

NASA Technical Reports Server (NTRS)

Glaze, L. S.; Stofan, E. R.; Smrekar, S. E.; Baloga, S. M.

2002-01-01

Statistical analysis of an expanded database of coronae on Venus indicates that the populations of Type 1 (with fracture annuli) and 2 (without fracture annuli) corona diameters are statistically indistinguishable, and therefore we have no basis for assuming different formation mechanisms. Analysis of the topography and diameters of coronae shows that coronae that are depressions, rimmed depressions, and domes tend to be significantly smaller than those that are plateaus, rimmed plateaus, or domes with surrounding rims. This is consistent with the model of Smrekar and Stofan and inconsistent with predictions of the spreading drop model of Koch and Manga. The diameter range for domes, the initial stage of corona formation, provides a broad constraint on the buoyancy of corona-forming plumes. Coronae are only slightly more likely to be topographically raised than depressions, with Type 1 coronae most frequently occurring as rimmed depressions and Type 2 coronae most frequently occuring with flat interiors and raised rims. Most Type 1 coronae are located along chasmata systems or fracture belts, while Type 2 coronas are found predominantly as isolated features in the plains. Coronae at hotspot rises tend to be significantly larger than coronae in other settings, consistent with a hotter upper mantle at hotspot rises and their active state.

DETECTING UNSPECIFIED STRUCTURE IN LOW-COUNT IMAGES

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

Stein, Nathan M.; Dyk, David A. van; Kashyap, Vinay L.

Unexpected structure in images of astronomical sources often presents itself upon visual inspection of the image, but such apparent structure may either correspond to true features in the source or be due to noise in the data. This paper presents a method for testing whether inferred structure in an image with Poisson noise represents a significant departure from a baseline (null) model of the image. To infer image structure, we conduct a Bayesian analysis of a full model that uses a multiscale component to allow flexible departures from the posited null model. As a test statistic, we use a tailmore » probability of the posterior distribution under the full model. This choice of test statistic allows us to estimate a computationally efficient upper bound on a p-value that enables us to draw strong conclusions even when there are limited computational resources that can be devoted to simulations under the null model. We demonstrate the statistical performance of our method on simulated images. Applying our method to an X-ray image of the quasar 0730+257, we find significant evidence against the null model of a single point source and uniform background, lending support to the claim of an X-ray jet.« less

Searching for oscillations in the primordial power spectrum. II. Constraints from Planck data

NASA Astrophysics Data System (ADS)

Meerburg, P. Daniel; Spergel, David N.; Wandelt, Benjamin D.

2014-03-01

In this second of two papers we apply our recently developed code to search for resonance features in the Planck CMB temperature data. We search both for log-spaced oscillations or linear-spaced oscillations and compare our findings with results of our WMAP9 analysis and the Planck team analysis [P. A. R. Ade et al. (Planck Collaboration>), arXiv:1303.5082]. While there are hints of log-spaced resonant features present in the WMAP9 data, the significance of these features weaken with more data. With more accurate small scale measurements, we also find that the best-fit frequency has shifted and the amplitude has been reduced. We confirm the presence of a several low frequency peaks, earlier identified by the Planck team, but with a better improvement of fit (Δχeff2˜12). We further investigate this improvement by allowing the lensing potential to vary as well, showing mild correlation between the amplitude of the oscillations and the lensing amplitude. We find that the improvement of the fit increases even more (Δχeff2˜14) for the low frequencies that modify the spectrum in a way that mimics the lensing effect. Since these features were not present in the WMAP data, they are primarily due to better measurements of Planck at small angular scales. For linear-spaced oscillations we find a maximum Δχeff2˜13 scanning two orders of magnitude in frequency space, and the biggest improvements are at extremely high frequencies. Again, we recover a best-fit frequency very close to the one found in WMAP9, which confirms that the fit improvement is driven by low ℓ. Further comparisons with WMAP9 show Planck contains many more features, both for linear- and log-spaced oscillations, but with a smaller improvement of fit. We discuss the improvement as a function of the number of modes and study the effect of the 217 GHz map, which appears to drive most of the improvement for log-spaced oscillations. Two points strongly suggest that the detected features are fitting a combination of the noise and the dip at ℓ˜1800 in the 217 GHz map: the fit improvement mostly comes from a small range of ℓ, and comparison with simulations shows that the fit improvement is consistent with a statistical fluctuation. We conclude that none of the detected features are statistically significant.

Qualitative Assessment of Ultrasound Biomicroscopic Images Using Standard Photographs: The Liwan Eye Study

PubMed Central

Jiang, Yuzhen; Huang, Wenyong; Huang, Qunxiao; Zhang, Jian; Foster, Paul J.

2010-01-01

Objective. To classify anatomic features related to anterior chamber angles by a qualitative assessment system based on ultrasound biomicroscopy (UBM) images. Methods. Cases of primary angle-closure suspect (PACS), defined by pigmented trabecular meshwork that is not visible in two or more quadrants on static gonioscopy (cases) and systematically selected subjects (1 of every 10) who did not meet this criterion (controls) were enrolled during a population-based survey in Guangzhou, China. All subjects underwent UBM examination. A set of standard UBM images was used to qualitatively classify anatomic features related to the angle configuration, including iris thickness, iris convexity, iris angulation, ciliary body size, and ciliary process position. All analysis was conducted on right eye images. Results. Based on the qualitative grades, the difference in overall iris thickness between gonioscopically narrow eyes (n = 117) and control eyes (n = 57) was not statistically significant. The peripheral one third of the iris tended to be thicker in all quadrants of the PACS eyes, although the difference was statistically significant only in the superior quadrant (P = 0.008). No significant differences were found in the qualitative classifications of iris insertion, iris angulation, ciliary body size, and ciliary process position. The findings were similar when compared with the control group of eyes with wide angles in all quadrants. Conclusions. Basal iris thickness seems to be more relevant to narrow angle configuration than to overall iris thickness. Otherwise, the anterior rotation and size of the ciliary body, the iris insertion, and the overall iris thickness are comparable in narrow- and wide-angle eyes. PMID:19834039

Evaluation of the mechanical behaviour of PathFile and ProGlider pathfinding nickel-titanium rotary instruments.

PubMed

Elnaghy, A M; Elsaka, S E

2015-09-01

To assess and compare the resistance to cyclic fatigue, torsional stress, bending and buckling of ProGlider (PG; Dentsply Maillefer, Ballaigues, Switzerland) instruments with PathFile (PF; Dentsply Maillefer) pathfinding nickel-titanium rotary instruments. Size 16, .02 taper PG and PF instruments were rotated in simulated canals until failure, and the number of cycles to failure (NCF) was recorded to evaluate their cyclic fatigue resistance. Torsional strength was measured using a torsiometer after fixing rigidly the apical 5 mm of the instrument. A scanning electron microscope was used to characterize the topographic features of the fracture surfaces of the instruments. The instruments were evaluated for bending resistance using a cantilever-bending test. The buckling resistance was measured by recording the maximum load required to form a lateral elastic displacement along the file axis using a universal testing machine. Data were statistically analysed using independent t-tests. Statistical significance was set at P < 0.05. ProGlider instrument had a significantly higher flexibility, higher resistance to cyclic fatigue and torsional stress than PF instruments (P < 0.05). The fractured cross-sectional surfaces revealed typical features of cyclic fatigue and torsional fractures. There was no significant difference in the maximum load needed to buckle the two instruments tested (P = 0.082). ProGlider NiTi pathfinding instrument manufactured from M-Wire alloy had enhanced mechanical properties, including higher flexibility, higher resistance to cyclic fatigue and torsional stress compared with PathFile instrument made of conventional NiTi alloy. © 2014 International Endodontic Journal. Published by John Wiley & Sons Ltd.

A link prediction approach to cancer drug sensitivity prediction.

PubMed

Turki, Turki; Wei, Zhi

2017-10-03

Predicting the response to a drug for cancer disease patients based on genomic information is an important problem in modern clinical oncology. This problem occurs in part because many available drug sensitivity prediction algorithms do not consider better quality cancer cell lines and the adoption of new feature representations; both lead to the accurate prediction of drug responses. By predicting accurate drug responses to cancer, oncologists gain a more complete understanding of the effective treatments for each patient, which is a core goal in precision medicine. In this paper, we model cancer drug sensitivity as a link prediction, which is shown to be an effective technique. We evaluate our proposed link prediction algorithms and compare them with an existing drug sensitivity prediction approach based on clinical trial data. The experimental results based on the clinical trial data show the stability of our link prediction algorithms, which yield the highest area under the ROC curve (AUC) and are statistically significant. We propose a link prediction approach to obtain new feature representation. Compared with an existing approach, the results show that incorporating the new feature representation to the link prediction algorithms has significantly improved the performance.

Relation between brain architecture and mathematical ability in children: a DBM study.

PubMed

Han, Zhaoying; Davis, Nicole; Fuchs, Lynn; Anderson, Adam W; Gore, John C; Dawant, Benoit M

2013-12-01

Population-based studies indicate that between 5 and 9 percent of US children exhibit significant deficits in mathematical reasoning, yet little is understood about the brain morphological features related to mathematical performances. In this work, deformation-based morphometry (DBM) analyses have been performed on magnetic resonance images of the brains of 79 third graders to investigate whether there is a correlation between brain morphological features and mathematical proficiency. Group comparison was also performed between Math Difficulties (MD-worst math performers) and Normal Controls (NC), where each subgroup consists of 20 age and gender matched subjects. DBM analysis is based on the analysis of the deformation fields generated by non-rigid registration algorithms, which warp the individual volumes to a common space. To evaluate the effect of registration algorithms on DBM results, five nonrigid registration algorithms have been used: (1) the Adaptive Bases Algorithm (ABA); (2) the Image Registration Toolkit (IRTK); (3) the FSL Nonlinear Image Registration Tool; (4) the Automatic Registration Tool (ART); and (5) the normalization algorithm available in SPM8. The deformation field magnitude (DFM) was used to measure the displacement at each voxel, and the Jacobian determinant (JAC) was used to quantify local volumetric changes. Results show there are no statistically significant volumetric differences between the NC and the MD groups using JAC. However, DBM analysis using DFM found statistically significant anatomical variations between the two groups around the left occipital-temporal cortex, left orbital-frontal cortex, and right insular cortex. Regions of agreement between at least two algorithms based on voxel-wise analysis were used to define Regions of Interest (ROIs) to perform an ROI-based correlation analysis on all 79 volumes. Correlations between average DFM values and standard mathematical scores over these regions were found to be significant. We also found that the choice of registration algorithm has an impact on DBM-based results, so we recommend using more than one algorithm when conducting DBM studies. To the best of our knowledge, this is the first study that uses DBM to investigate brain anatomical features related to mathematical performance in a relatively large population of children. © 2013.

Analysis and Recognition of Traditional Chinese Medicine Pulse Based on the Hilbert-Huang Transform and Random Forest in Patients with Coronary Heart Disease

PubMed Central

Wang, Yiqin; Yan, Hanxia; Yan, Jianjun; Yuan, Fengyin; Xu, Zhaoxia; Liu, Guoping; Xu, Wenjie

2015-01-01

Objective. This research provides objective and quantitative parameters of the traditional Chinese medicine (TCM) pulse conditions for distinguishing between patients with the coronary heart disease (CHD) and normal people by using the proposed classification approach based on Hilbert-Huang transform (HHT) and random forest. Methods. The energy and the sample entropy features were extracted by applying the HHT to TCM pulse by treating these pulse signals as time series. By using the random forest classifier, the extracted two types of features and their combination were, respectively, used as input data to establish classification model. Results. Statistical results showed that there were significant differences in the pulse energy and sample entropy between the CHD group and the normal group. Moreover, the energy features, sample entropy features, and their combination were inputted as pulse feature vectors; the corresponding average recognition rates were 84%, 76.35%, and 90.21%, respectively. Conclusion. The proposed approach could be appropriately used to analyze pulses of patients with CHD, which can lay a foundation for research on objective and quantitative criteria on disease diagnosis or Zheng differentiation. PMID:26180536

Analysis and Recognition of Traditional Chinese Medicine Pulse Based on the Hilbert-Huang Transform and Random Forest in Patients with Coronary Heart Disease.

PubMed

Guo, Rui; Wang, Yiqin; Yan, Hanxia; Yan, Jianjun; Yuan, Fengyin; Xu, Zhaoxia; Liu, Guoping; Xu, Wenjie

2015-01-01

Objective. This research provides objective and quantitative parameters of the traditional Chinese medicine (TCM) pulse conditions for distinguishing between patients with the coronary heart disease (CHD) and normal people by using the proposed classification approach based on Hilbert-Huang transform (HHT) and random forest. Methods. The energy and the sample entropy features were extracted by applying the HHT to TCM pulse by treating these pulse signals as time series. By using the random forest classifier, the extracted two types of features and their combination were, respectively, used as input data to establish classification model. Results. Statistical results showed that there were significant differences in the pulse energy and sample entropy between the CHD group and the normal group. Moreover, the energy features, sample entropy features, and their combination were inputted as pulse feature vectors; the corresponding average recognition rates were 84%, 76.35%, and 90.21%, respectively. Conclusion. The proposed approach could be appropriately used to analyze pulses of patients with CHD, which can lay a foundation for research on objective and quantitative criteria on disease diagnosis or Zheng differentiation.

A Novel Fiber Optic Based Surveillance System for Prevention of Pipeline Integrity Threats.

PubMed

Tejedor, Javier; Macias-Guarasa, Javier; Martins, Hugo F; Piote, Daniel; Pastor-Graells, Juan; Martin-Lopez, Sonia; Corredera, Pedro; Gonzalez-Herraez, Miguel

2017-02-12

This paper presents a novel surveillance system aimed at the detection and classification of threats in the vicinity of a long gas pipeline. The sensing system is based on phase-sensitive optical time domain reflectometry ( ϕ -OTDR) technology for signal acquisition and pattern recognition strategies for threat identification. The proposal incorporates contextual information at the feature level and applies a system combination strategy for pattern classification. The contextual information at the feature level is based on the tandem approach (using feature representations produced by discriminatively-trained multi-layer perceptrons) by employing feature vectors that spread different temporal contexts. The system combination strategy is based on a posterior combination of likelihoods computed from different pattern classification processes. The system operates in two different modes: (1) machine + activity identification, which recognizes the activity being carried out by a certain machine, and (2) threat detection, aimed at detecting threats no matter what the real activity being conducted is. In comparison with a previous system based on the same rigorous experimental setup, the results show that the system combination from the contextual feature information improves the results for each individual class in both operational modes, as well as the overall classification accuracy, with statistically-significant improvements.

Nonlinear wave chaos: statistics of second harmonic fields.

PubMed

Zhou, Min; Ott, Edward; Antonsen, Thomas M; Anlage, Steven M

2017-10-01

Concepts from the field of wave chaos have been shown to successfully predict the statistical properties of linear electromagnetic fields in electrically large enclosures. The Random Coupling Model (RCM) describes these properties by incorporating both universal features described by Random Matrix Theory and the system-specific features of particular system realizations. In an effort to extend this approach to the nonlinear domain, we add an active nonlinear frequency-doubling circuit to an otherwise linear wave chaotic system, and we measure the statistical properties of the resulting second harmonic fields. We develop an RCM-based model of this system as two linear chaotic cavities coupled by means of a nonlinear transfer function. The harmonic field strengths are predicted to be the product of two statistical quantities and the nonlinearity characteristics. Statistical results from measurement-based calculation, RCM-based simulation, and direct experimental measurements are compared and show good agreement over many decades of power.

Blended particle filters for large-dimensional chaotic dynamical systems

PubMed Central

Majda, Andrew J.; Qi, Di; Sapsis, Themistoklis P.

2014-01-01

A major challenge in contemporary data science is the development of statistically accurate particle filters to capture non-Gaussian features in large-dimensional chaotic dynamical systems. Blended particle filters that capture non-Gaussian features in an adaptively evolving low-dimensional subspace through particles interacting with evolving Gaussian statistics on the remaining portion of phase space are introduced here. These blended particle filters are constructed in this paper through a mathematical formalism involving conditional Gaussian mixtures combined with statistically nonlinear forecast models compatible with this structure developed recently with high skill for uncertainty quantification. Stringent test cases for filtering involving the 40-dimensional Lorenz 96 model with a 5-dimensional adaptive subspace for nonlinear blended filtering in various turbulent regimes with at least nine positive Lyapunov exponents are used here. These cases demonstrate the high skill of the blended particle filter algorithms in capturing both highly non-Gaussian dynamical features as well as crucial nonlinear statistics for accurate filtering in extreme filtering regimes with sparse infrequent high-quality observations. The formalism developed here is also useful for multiscale filtering of turbulent systems and a simple application is sketched below. PMID:24825886

Origin of the correlations between exit times in pedestrian flows through a bottleneck

NASA Astrophysics Data System (ADS)

Nicolas, Alexandre; Touloupas, Ioannis

2018-01-01

Robust statistical features have emerged from the microscopic analysis of dense pedestrian flows through a bottleneck, notably with respect to the time gaps between successive passages. We pinpoint the mechanisms at the origin of these features thanks to simple models that we develop and analyse quantitatively. We disprove the idea that anticorrelations between successive time gaps (i.e. an alternation between shorter ones and longer ones) are a hallmark of a zipper-like intercalation of pedestrian lines and show that they simply result from the possibility that pedestrians from distinct ‘lines’ or directions cross the bottleneck within a short time interval. A second feature concerns the bursts of escapes, i.e. egresses that come in fast succession. Despite the ubiquity of exponential distributions of burst sizes, entailed by a Poisson process, we argue that anomalous (power-law) statistics arise if the bottleneck is nearly congested, albeit only in a tiny portion of parameter space. The generality of the proposed mechanisms implies that similar statistical features should also be observed for other types of particulate flows.

Machine learning to analyze images of shocked materials for precise and accurate measurements

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

Dresselhaus-Cooper, Leora; Howard, Marylesa; Hock, Margaret C.

A supervised machine learning algorithm, called locally adaptive discriminant analysis (LADA), has been developed to locate boundaries between identifiable image features that have varying intensities. LADA is an adaptation of image segmentation, which includes techniques that find the positions of image features (classes) using statistical intensity distributions for each class in the image. In order to place a pixel in the proper class, LADA considers the intensity at that pixel and the distribution of intensities in local (nearby) pixels. This paper presents the use of LADA to provide, with statistical uncertainties, the positions and shapes of features within ultrafast imagesmore » of shock waves. We demonstrate the ability to locate image features including crystals, density changes associated with shock waves, and material jetting caused by shock waves. This algorithm can analyze images that exhibit a wide range of physical phenomena because it does not rely on comparison to a model. LADA enables analysis of images from shock physics with statistical rigor independent of underlying models or simulations.« less

Robust kernel representation with statistical local features for face recognition.

PubMed

Yang, Meng; Zhang, Lei; Shiu, Simon Chi-Keung; Zhang, David

2013-06-01

Factors such as misalignment, pose variation, and occlusion make robust face recognition a difficult problem. It is known that statistical features such as local binary pattern are effective for local feature extraction, whereas the recently proposed sparse or collaborative representation-based classification has shown interesting results in robust face recognition. In this paper, we propose a novel robust kernel representation model with statistical local features (SLF) for robust face recognition. Initially, multipartition max pooling is used to enhance the invariance of SLF to image registration error. Then, a kernel-based representation model is proposed to fully exploit the discrimination information embedded in the SLF, and robust regression is adopted to effectively handle the occlusion in face images. Extensive experiments are conducted on benchmark face databases, including extended Yale B, AR (A. Martinez and R. Benavente), multiple pose, illumination, and expression (multi-PIE), facial recognition technology (FERET), face recognition grand challenge (FRGC), and labeled faces in the wild (LFW), which have different variations of lighting, expression, pose, and occlusions, demonstrating the promising performance of the proposed method.

Learning Scene Categories from High Resolution Satellite Image for Aerial Video Analysis

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

Cheriyadat, Anil M

2011-01-01

Automatic scene categorization can benefit various aerial video processing applications. This paper addresses the problem of predicting the scene category from aerial video frames using a prior model learned from satellite imagery. We show that local and global features in the form of line statistics and 2-D power spectrum parameters respectively can characterize the aerial scene well. The line feature statistics and spatial frequency parameters are useful cues to distinguish between different urban scene categories. We learn the scene prediction model from highresolution satellite imagery to test the model on the Columbus Surrogate Unmanned Aerial Vehicle (CSUAV) dataset ollected bymore » high-altitude wide area UAV sensor platform. e compare the proposed features with the popular Scale nvariant Feature Transform (SIFT) features. Our experimental results show that proposed approach outperforms te SIFT model when the training and testing are conducted n disparate data sources.« less

An investigation to improve the Menhaden fishery prediction and detection model through the application of ERTS-A data

NASA Technical Reports Server (NTRS)

Maughan, P. M. (Principal Investigator)

1972-01-01

The author has identified the following significant results. Preliminary analyses indicate that several important relationships have been observed utilizing ERTS-1 imagery. Of most significance is that in the Mississippi Sound, as elsewhere, considerable detail exists as to turbidity patterns in the water column. Simple analysis is complicated by the apparent interaction between actual turbidity, turbidity induced by shoal water, and actual imaging of the bottom in extreme shoal water. A statistical approach is being explored which shows promise of at least partially separating these effects so that partitioning of true turbid plumes can be accomplished. This partitioning is of great importance to this program in that supportive data seem to indicate that menhaden occur more frequently in turbid areas. In this connection four individual captures have been associated with a major turbid feature imaged on 6 August. If a significant relationship between imaged turbid features and catch distribution can be established, for example by graphic and/or numeric analysis, it will represent a major advancement for short term prediction of commercially accessible menhaden.

Social anxiety and the severity and typography of stuttering in adolescents.

PubMed

Mulcahy, Kylie; Hennessey, Neville; Beilby, Janet; Byrnes, Michelle

2008-12-01

The present study examined the relationship between anxiety, attitude toward daily communication, and stuttering symptomatology in adolescent stuttering. Adolescents who stuttered (n=19) showed significantly higher levels of trait, state and social anxiety than fluent speaking controls (n=18). Trait and state anxiety was significantly associated with difficulty with communication in daily situations for adolescents who stutter, but not for controls. No statistically significant associations were found between anxiety and measures of communication difficulty, and the severity or typography of stuttering surface behaviours. These results highlight some of the psychosocial concomitants of chronic stuttering in adolescence, but challenge the notion that anxiety plays a direct mediating role in stuttering surface behaviours. Rather, the results suggest stuttering is a disorder that features psychosocial conflict regardless of its surface features. The reader will be able to: (1) summarise findings from previous studies with regards to stuttering and anxiety; (2) identify the sub-types of anxiety that may impact on the individual who stutters; and (3) discuss the clinical implications of the results with regards to working with adolescents who stutter.

Personality disorders among patients with panic disorder and individuals with high anxiety sensitivity.

PubMed

Osma, Jorge; García-Palacios, Azucena; Botella, Cristina; Barrada, Juan Ramón

2014-05-01

No studies have been found that compared the psychopathology features, including personality disorders, of Panic Disorder (PD) and Panic Disorder with Agoraphobia (PDA), and a nonclinical sample with anxiety vulnerability. The total sample included 152 participants, 52 in the PD/PDA, 45 in the high anxiety sensitivity (AS) sample, and 55 in the nonclinical sample. The participants in PD/PDA sample were evaluated with the structured interview ADIS-IV. The Brief Symptom Inventory and the MCMI-III were used in all three samples. Statistically significant differences were found between the PD/PDA and the nonclinical sample in all MCMI-III scales except for antisocial and compulsive. No significant differences were found between PD/PDA and the sample with high scores in AS. Phobic Anxiety and Paranoid Ideation were the only scales where there were significant differences between the PD/PDA sample and the high AS sample. Our findings showed that people who scored high on AS, despite not having a diagnosis of PD/PDA, were similar in regard to psychopathology features and personality to individuals with PD/PDA.

Feature Statistics Modulate the Activation of Meaning During Spoken Word Processing.

PubMed

Devereux, Barry J; Taylor, Kirsten I; Randall, Billi; Geertzen, Jeroen; Tyler, Lorraine K

2016-03-01

Understanding spoken words involves a rapid mapping from speech to conceptual representations. One distributed feature-based conceptual account assumes that the statistical characteristics of concepts' features--the number of concepts they occur in (distinctiveness/sharedness) and likelihood of co-occurrence (correlational strength)--determine conceptual activation. To test these claims, we investigated the role of distinctiveness/sharedness and correlational strength in speech-to-meaning mapping, using a lexical decision task and computational simulations. Responses were faster for concepts with higher sharedness, suggesting that shared features are facilitatory in tasks like lexical decision that require access to them. Correlational strength facilitated responses for slower participants, suggesting a time-sensitive co-occurrence-driven settling mechanism. The computational simulation showed similar effects, with early effects of shared features and later effects of correlational strength. These results support a general-to-specific account of conceptual processing, whereby early activation of shared features is followed by the gradual emergence of a specific target representation. Copyright © 2015 The Authors. Cognitive Science published by Cognitive Science Society, Inc.

GASP cloud- and particle-encounter statistics and their application to LPC aircraft studies. Volume 1: Analysis and conclusions

NASA Technical Reports Server (NTRS)

Jasperson, W. H.; Nastrom, G. D.; Davis, R. E.; Holdeman, J. D.

1984-01-01

Summary studies are presented for the entire cloud observation archieve from the NASA Global Atmospheric Sampling Program (GASP). Studies are also presented for GASP particle concentration data gathered concurrently with the cloud observations. Cloud encounters are shown on about 15 percent of the data samples overall, but the probability of cloud encounter is shown to vary significantly with altitude, latitude, and distance from the tropopause. Several meteorological circulation features are apparent in the latitudinal distribution of cloud cover, and the cloud encounter statistics are shown to be consistent with the classical mid-latitude cyclone model. Observations of clouds spaced more closely than 90 minutes are shown to be statistically dependent. The statistics for cloud and particle encounter are utilized to estimate the frequency of cloud encounter on long range airline routes, and to assess the probability and extent of laminar flow loss due to cloud or particle encounter by aircraft utilizing laminar flow control (LFC). It is shown that the probability of extended cloud encounter is too low, of itself, to make LFC impractical.

Machine learning vortices at the Kosterlitz-Thouless transition

NASA Astrophysics Data System (ADS)

Beach, Matthew J. S.; Golubeva, Anna; Melko, Roger G.

2018-01-01

Efficient and automated classification of phases from minimally processed data is one goal of machine learning in condensed-matter and statistical physics. Supervised algorithms trained on raw samples of microstates can successfully detect conventional phase transitions via learning a bulk feature such as an order parameter. In this paper, we investigate whether neural networks can learn to classify phases based on topological defects. We address this question on the two-dimensional classical XY model which exhibits a Kosterlitz-Thouless transition. We find significant feature engineering of the raw spin states is required to convincingly claim that features of the vortex configurations are responsible for learning the transition temperature. We further show a single-layer network does not correctly classify the phases of the XY model, while a convolutional network easily performs classification by learning the global magnetization. Finally, we design a deep network capable of learning vortices without feature engineering. We demonstrate the detection of vortices does not necessarily result in the best classification accuracy, especially for lattices of less than approximately 1000 spins. For larger systems, it remains a difficult task to learn vortices.

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

Altazi, B; Fernandez, D; Zhang, G

Purpose: Radiomics have shown potential for predicting treatment outcomes in several body sites. This study investigated the correlation between PET Radiomics features and treatment response of cervical cancer outcomes. Methods: our dataset consisted of a cohort of 79 patients diagnosed with cervical cancer, FIGO stage IB-IVA, age range 25–86 years, (median age at diagnosis: 50 years) all treated between: 2009–14 with external beam radiation therapy to a dose range between: 45–50.4 Gy (median= 45 Gy), concurrent cisplatin chemotherapy and MRI-based brachytherapy to a dose of 20–30 Gy (median= 28 Gy). Metabolic Tumor Volume (MTV) in patient’s primary site was delineatedmore » on pretreatment PET/CT by two board certified Radiation Oncologists. The features extracted from each patient’s volume were: 26 Co-occurrence matrix (COM) Feature, 11 Run-Length Matrix (RLM), 11 Gray Level Size Zone Matrix (GLSZM) and 33 Intensity-based features (IBF). The treatment outcome was divided based on the last follow up status into three classes: No Evidence of Disease (NED), Alive with Disease (AWD) and Dead of Disease (DOD). The ability for the radiomics features to differentiate between the 3 treatments outcome categories were assessed by One-Way ANOVA test with p-value < 0.05 was to be statistically significant. The results from the analysis were compared with the ones obtained previously for standard Uptake Value (SUV). Results: Based on patients last clinical follow-up; 52 showed NED, 17 AWD and 10 DOD. Radiomics Features were able to classify the patients based on their treatment response. A parallel analysis was done for SUV measurements for comparison. Conclusion: Radiomics features were able to differentiate between the three different classes of treatment outcomes. However, most of the features were only able to differentiate between NED and DOD class. Also, The ability or radiomics features to differentiate types of response were more significant than SUV.« less

Towards intelligent diagnostic system employing integration of mathematical and engineering model

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

Isa, Nor Ashidi Mat

The development of medical diagnostic system has been one of the main research fields during years. The goal of the medical diagnostic system is to place a nosological system that could ease the diagnostic evaluation normally performed by scientists and doctors. Efficient diagnostic evaluation is essentials and requires broad knowledge in order to improve conventional diagnostic system. Several approaches on developing the medical diagnostic system have been designed and tested since the earliest 60s. Attempts on improving their performance have been made which utilizes the fields of artificial intelligence, statistical analyses, mathematical model and engineering theories. With the availability ofmore » the microcomputer and software development as well as the promising aforementioned fields, medical diagnostic prototypes could be developed. In general, the medical diagnostic system consists of several stages, namely the 1) data acquisition, 2) feature extraction, 3) feature selection, and 4) classifications stages. Data acquisition stage plays an important role in converting the inputs measured from the real world physical conditions to the digital numeric values that can be manipulated by the computer system. One of the common medical inputs could be medical microscopic images, radiographic images, magnetic resonance image (MRI) as well as medical signals such as electrocardiogram (ECG) and electroencephalogram (EEG). Normally, the scientist or doctors have to deal with myriad of data and redundant to be processed. In order to reduce the complexity of the diagnosis process, only the significant features of the raw data such as peak value of the ECG signal or size of lesion in the mammogram images will be extracted and considered in the subsequent stages. Mathematical models and statistical analyses will be performed to select the most significant features to be classified. The statistical analyses such as principal component analysis and discriminant analysis as well as mathematical model of clustering technique have been widely used in developing the medical diagnostic systems. The selected features will be classified using mathematical models that embedded engineering theory such as artificial intelligence, support vector machine, neural network and fuzzy-neuro system. These classifiers will provide the diagnostic results without human intervention. Among many publishable researches, several prototypes have been developed namely NeuralPap, Neural Mammo, and Cervix Kit. The former system (NeuralPap) is an automatic intelligent diagnostic system for classifying and distinguishing between the normal and cervical cancerous cells. Meanwhile, the Cervix Kit is a portable Field-programmable gate array (FPGA)-based cervical diagnostic kit that could automatically diagnose the cancerous cell based on the images obtained during sampling test. Besides the cervical diagnostic system, the Neural Mammo system is developed to specifically aid the diagnosis of breast cancer using a fine needle aspiration image.« less

Towards intelligent diagnostic system employing integration of mathematical and engineering model

NASA Astrophysics Data System (ADS)

Isa, Nor Ashidi Mat

2015-05-01

The development of medical diagnostic system has been one of the main research fields during years. The goal of the medical diagnostic system is to place a nosological system that could ease the diagnostic evaluation normally performed by scientists and doctors. Efficient diagnostic evaluation is essentials and requires broad knowledge in order to improve conventional diagnostic system. Several approaches on developing the medical diagnostic system have been designed and tested since the earliest 60s. Attempts on improving their performance have been made which utilizes the fields of artificial intelligence, statistical analyses, mathematical model and engineering theories. With the availability of the microcomputer and software development as well as the promising aforementioned fields, medical diagnostic prototypes could be developed. In general, the medical diagnostic system consists of several stages, namely the 1) data acquisition, 2) feature extraction, 3) feature selection, and 4) classifications stages. Data acquisition stage plays an important role in converting the inputs measured from the real world physical conditions to the digital numeric values that can be manipulated by the computer system. One of the common medical inputs could be medical microscopic images, radiographic images, magnetic resonance image (MRI) as well as medical signals such as electrocardiogram (ECG) and electroencephalogram (EEG). Normally, the scientist or doctors have to deal with myriad of data and redundant to be processed. In order to reduce the complexity of the diagnosis process, only the significant features of the raw data such as peak value of the ECG signal or size of lesion in the mammogram images will be extracted and considered in the subsequent stages. Mathematical models and statistical analyses will be performed to select the most significant features to be classified. The statistical analyses such as principal component analysis and discriminant analysis as well as mathematical model of clustering technique have been widely used in developing the medical diagnostic systems. The selected features will be classified using mathematical models that embedded engineering theory such as artificial intelligence, support vector machine, neural network and fuzzy-neuro system. These classifiers will provide the diagnostic results without human intervention. Among many publishable researches, several prototypes have been developed namely NeuralPap, Neural Mammo, and Cervix Kit. The former system (NeuralPap) is an automatic intelligent diagnostic system for classifying and distinguishing between the normal and cervical cancerous cells. Meanwhile, the Cervix Kit is a portable Field-programmable gate array (FPGA)-based cervical diagnostic kit that could automatically diagnose the cancerous cell based on the images obtained during sampling test. Besides the cervical diagnostic system, the Neural Mammo system is developed to specifically aid the diagnosis of breast cancer using a fine needle aspiration image.

Less is more: Avoiding the LIBS dimensionality curse through judicious feature selection for explosive detection.

PubMed

Kumar Myakalwar, Ashwin; Spegazzini, Nicolas; Zhang, Chi; Kumar Anubham, Siva; Dasari, Ramachandra R; Barman, Ishan; Kumar Gundawar, Manoj

2015-08-19

Despite its intrinsic advantages, translation of laser induced breakdown spectroscopy for material identification has been often impeded by the lack of robustness of developed classification models, often due to the presence of spurious correlations. While a number of classifiers exhibiting high discriminatory power have been reported, efforts in establishing the subset of relevant spectral features that enable a fundamental interpretation of the segmentation capability and avoid the 'curse of dimensionality' have been lacking. Using LIBS data acquired from a set of secondary explosives, we investigate judicious feature selection approaches and architect two different chemometrics classifiers -based on feature selection through prerequisite knowledge of the sample composition and genetic algorithm, respectively. While the full spectral input results in classification rate of ca.92%, selection of only carbon to hydrogen spectral window results in near identical performance. Importantly, the genetic algorithm-derived classifier shows a statistically significant improvement to ca. 94% accuracy for prospective classification, even though the number of features used is an order of magnitude smaller. Our findings demonstrate the impact of rigorous feature selection in LIBS and also hint at the feasibility of using a discrete filter based detector thereby enabling a cheaper and compact system more amenable to field operations.

Less is more: Avoiding the LIBS dimensionality curse through judicious feature selection for explosive detection

PubMed Central

Kumar Myakalwar, Ashwin; Spegazzini, Nicolas; Zhang, Chi; Kumar Anubham, Siva; Dasari, Ramachandra R.; Barman, Ishan; Kumar Gundawar, Manoj

2015-01-01

Despite its intrinsic advantages, translation of laser induced breakdown spectroscopy for material identification has been often impeded by the lack of robustness of developed classification models, often due to the presence of spurious correlations. While a number of classifiers exhibiting high discriminatory power have been reported, efforts in establishing the subset of relevant spectral features that enable a fundamental interpretation of the segmentation capability and avoid the ‘curse of dimensionality’ have been lacking. Using LIBS data acquired from a set of secondary explosives, we investigate judicious feature selection approaches and architect two different chemometrics classifiers –based on feature selection through prerequisite knowledge of the sample composition and genetic algorithm, respectively. While the full spectral input results in classification rate of ca.92%, selection of only carbon to hydrogen spectral window results in near identical performance. Importantly, the genetic algorithm-derived classifier shows a statistically significant improvement to ca. 94% accuracy for prospective classification, even though the number of features used is an order of magnitude smaller. Our findings demonstrate the impact of rigorous feature selection in LIBS and also hint at the feasibility of using a discrete filter based detector thereby enabling a cheaper and compact system more amenable to field operations. PMID:26286630

[Clinical study on vocal cords spontaneous rehabilitation after CO2 laser surgery].

PubMed

Zhang, Qingxiang; Hu, Huiying; Sun, Guoyan; Yu, Zhenkun

2014-10-01

To study the spontaneous rehabilitation and phonation quality of vocal cords after different types of CO2 laser microsurgery. Surgical procedures based on Remacle system Type I, Type II, Type III, Type IV and Type V a respectively. Three hundred and fifteen cases with hoarseness based on strobe laryngoscopy results were prospectively assigned to different group according to vocal lesions apperence,vocal vibration and imaging of larynx CT/MRI. Each group holded 63 cases. The investigation included the vocal cords morphological features,the patients' subjective feelings and objective results of vocal cords. There are no severe complications for all patients in perioperative period. Vocal scar found in Type I ,1 case; Type II, 9 cases ;Type III, 47 cases; Type IV, 61 cases and Type Va 63 cases respectively after surgery. The difference of Vocal scar formation after surgery between surgical procedures are statistical significance (χ2 = 222.24, P < 0.05). Hoarseness improved after the surgery in 59 cases of Type I , 51 cases of Type II, 43 cases of Type III, 21 cases of Type IV and 17 cases of Type Va. There are statistically significance (χ2 = 89.46, P < 0.05) between different surgical procedures. The parameters of strobe laryngoscope: there are statistical significance on jitter between procedures (F 44.51, P < 0.05), but without difference within Type I and Type II (P > 0.05). This happened in shimmer parameter and the maximum phonation time (MPT) as jitter. There are no statistical significance between Type IV and Type Va on MPT (P > 0.05). Morphological and functional rehabilitation of vocal cord will be affected obviously when the body layer is injured. The depth and range of the CO2 laser microsurgery are the key factors affecting the vocal rehabilitation.

An analysis of science versus pseudoscience

NASA Astrophysics Data System (ADS)

Hooten, James T.

2011-12-01

This quantitative study identified distinctive features in archival datasets commissioned by the National Science Foundation (NSF) for Science and Engineering Indicators reports. The dependent variables included education level, and scores for science fact knowledge, science process knowledge, and pseudoscience beliefs. The dependent variables were aggregated into nine NSF-defined geographic regions and examined for the years 2004 and 2006. The variables were also examined over all years available in the dataset. Descriptive statistics were determined and tests for normality and homogeneity of variances were performed using Statistical Package for the Social Sciences. Analysis of Variance was used to test for statistically significant differences between the nine geographic regions for each of the four dependent variables. Statistical significance of 0.05 was used. Tukey post-hoc analysis was used to compute practical significance of differences between regions. Post-hoc power analysis using G*Power was used to calculate the probability of Type II errors. Tests for correlations across all years of the dependent variables were also performed. Pearson's r was used to indicate the strength of the relationship between the dependent variables. Small to medium differences in science literacy and education level were observed between many of the nine U.S. geographic regions. The most significant differences occurred when the West South Central region was compared to the New England and the Pacific regions. Belief in pseudoscience appeared to be distributed evenly across all U.S. geographic regions. Education level was a strong indicator of science literacy regardless of a respondent's region of residence. Recommendations for further study include more in-depth investigation to uncover the nature of the relationship between education level and belief in pseudoscience.

The investigation of the some body parameters of obese and (obese+diabetes) patients with using bioelectrical impedance analysis techniques

NASA Astrophysics Data System (ADS)

Yerlikaya, Emrah; Karageçili, Hasan; Aydin, Ruken Zeynep

2016-04-01

Obesity is a key risk for the development of hyperglycemia, hypertension, hyperlipidemia, insulin resistance and is totally referred to as the metabolic disorders. Diabetes mellitus, a metabolic disorder, is related with hyperglycemia, altered metabolism of lipids, carbohydrates and proteins. The minimum defining characteristic feature to identify diabetes mellitus is chronic and substantiated elevation of circulating glucose concentration. In this study, it is aimed to determine the body composition analyze of obese and (obese+diabetes) patients.We studied the datas taken from three independent groups with the body composition analyzer instrument. The body composition analyzer calculates body parameters, such as body fat ratio, body fat mass, fat free mass, estimated muscle mass, and base metabolic rate on the basis of data obtained by Dual Energy X-ray Absorptiometry using Bioelectrical Impedance Analysis. All patients and healthy subjects applied to Siirt University Medico and their datas were taken. The Statistical Package for Social Sciences version 21 was used for descriptive data analysis. When we compared and analyzed three groups datas, we found statistically significant difference between obese, (obese+diabetes) and control groups values. Anova test and tukey test are used to analyze the difference between groups and to do multiple comparisons. T test is also used to analyze the difference between genders. We observed the statistically significant difference in age and mineral amount p<0.00 between (diabetes+obese) and obese groups. Besides, when these patient groups and control group were analyzed, there were significant difference between most parameters. In terms of education level among the illiterate and university graduates; fat mass kg, fat percentage, internal lubrication, body mass index, water percentage, protein mass percentage, mineral percentage p<0.05, significant statistically difference were observed. This difference especially may result of a sedentary lifestyle.

Comparison of Surgically Induced Astigmatism and Morphologic Features Resulting From Femtosecond Laser and Manual Clear Corneal Incisions for Cataract Surgery.

PubMed

Ferreira, Tiago B; Ribeiro, Filomena J; Pinheiro, João; Ribeiro, Paulo; O'Neill, João G

2018-05-01

To compare the surgically induced astigmatism (SIA) vector, flattening effect, torque, and wound architecture following femtosecond laser and manual clear corneal incisions (CCIs). In a double-armed, randomized, prospective case series, cataract surgery was performed for 600 eyes using femtosecond laser (300 eyes) or manual (300 eyes) 2.4-mm CCIs in temporal or superior oblique locations. SIA, flattening effect, torque, and the summated vector mean for SIA were calculated. Correlation with individual features was established and incision morphology was investigated by anterior segment optical coherence tomography at 3 months of follow-up. The SIA, flattening effect, and torque were lower in the femtosecond laser group for both incision locations, although the differences were not significant (all P > .05). The femtosecond laser group showed less dispersion of SIA magnitude and flattening effect. Temporal and superior oblique incisions resulted in flattening effect values of -0.11 and -0.21 diopters (D), respectively, in the femtosecond laser group and -0.13 and -0.34 D, respectively, in the manual group. Significant correlations with individual features were only found in the femtosecond laser group, with preoperative astigmatism being the only significant SIA predictor by multiple regression analysis (P = .003). Femtosecond laser CCIs showed less deviation from the intended length, wound enlargement, endothelial misalignment, and Descemet membrane detachments (all P < .037). Femtosecond laser CCIs were more reproducible. Although SIAs were smaller in femtosecond laser CCIs than in manual CCIs for both temporal and superior oblique incisions, the difference was not statistically significant. Association with individual features is highly variable. [J Refract Surg. 2018;34(5):322-329.]. Copyright 2018, SLACK Incorporated.

Comparison of conventional and automated breast volume ultrasound in the description and characterization of solid breast masses based on BI-RADS features.

PubMed

Kim, Hyunji; Cha, Joo Hee; Oh, Ha-Yeun; Kim, Hak Hee; Shin, Hee Jung; Chae, Eun Young

2014-07-01

To compare the performance of radiologists in the use of conventional ultrasound (US) and automated breast volume ultrasound (ABVU) for the characterization of benign and malignant solid breast masses based on breast imaging and reporting data system (BI-RADS) criteria. Conventional US and ABVU images were obtained in 87 patients with 106 solid breast masses (52 cancers, 54 benign lesions). Three experienced radiologists who were blinded to all examination results independently characterized the lesions and reported a BI-RADS assessment category and a level of suspicion of malignancy. The results were analyzed by calculation of Cohen's κ coefficient and by receiver operating characteristic (ROC) analysis. Assessment of the agreement of conventional US and ABVU indicated that the posterior echo feature was the most discordant feature of seven features (κ = 0.371 ± 0.225) and that orientation had the greatest agreement (κ = 0.608 ± 0.210). The final assessment showed substantial agreement (κ = 0.773 ± 0.104). The areas under the ROC curves (Az) for conventional US and ABVU were not statistically significant for each reader, but the mean Az values of conventional US and ABVU by multi-reader multi-case analysis were significantly different (conventional US 0.991, ABVU 0.963; 95 % CI -0.0471 to -0.0097). The means for sensitivity, specificity, positive predictive value, and negative predictive value of conventional US and ABVU did not differ significantly. There was substantial inter-observer agreement in the final assessment of solid breast masses by conventional US and ABVU. ROC analysis comparing the performance of conventional US and ABVU indicated a marginally significant difference in mean Az, but not in mean sensitivity, specificity, positive predictive value, or negative predictive value.

Pap-tests with non-hyperchromatic dyskariosis are often associated with squamous intraepithelial lesions of the cervix uteri with eosinophilic features.

PubMed

Bellisano, Giulia; Ambrosini-Spaltro, Andrea; Faa, Gavino; Ravarino, Alberto; Piccin, Andrea; Peer, Irmgard; Kasal, Armin; Vittadello, Fabio; Negri, Giovanni

2016-10-01

Squamous intraepithelial lesions of the cervix uteri with eosinophilic features (eosinophilic dysplasia, ED) are a peculiar type of dysplasia with metaplastic phenotype which was described in histological specimens. The cytological features of these lesions have not been studied yet. Histological samples from 66 women with features of ED and positive p16(INK4a) staining were included in the study. Within the previous year, all women had at least one pap-test, whose features were recorded and compared with 31 control samples with high-grade dysplasia of usual type. The previous pap-test showed high-grade dysplastic cells with non-hyperchromatic nuclei in 56/66 (84.8%) cases and metaplastic features in 60/66 (90.9%) cases. Conversely, the dysplastic cells of the usual lesions showed non-hyperchromatic nuclei in 6/31 (19.4%) and metaplastic features in 4/31 (12.9%) cases. Statistical analysis showed significant differences in distribution of the non-hyperchromatic nuclei (P < 0.001), metaplastic features (P < 0.001), presence of both non-hyperchromatic nuclei and metaplastic features (P < 0.001) and usual dysplastic features (P < 0.001) among the study and control groups. A high-grade squamous intraepithelial lesion with non-hyperchromatic nuclei or metaplastic features is often found in the pap-test previous to the histological diagnosis of ED and may represent the cytologic correlate of this particular type of dysplasia. Diagn. Cytopathol. 2016;44:783-786. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Modified gastroduodenostomy in laparoscopy-assisted distal gastrectomy: a 'tornado' anastomosis.

PubMed

Kubota, Keisuke; Kuroda, Junko; Yoshida, Masashi; Okada, Akihiro; Nitori, Nobuhiro; Kitajima, Masaki

2013-01-01

This study was to examine the utility of a modified double-stapling end-to-end gastroduodenostomy method ('Tornado' anastomosis) compared to a method with an additional gastrotomy ('Anterior Incision' method) in laparoscopy-assisted distal gastrectomy. Forty-two patients with gastric cancer who underwent laparoscopy-assisted distal gastrectomy were analyzed retrospectively. Billroth-I using an additional gastrotomy was performed in 24 patients (AI group) and Billroth-I without an additional gastrotomy was performed in 18 (TOR group). Clinicopathological features, operative outcomes (lymph node dissection, operative time, operative blood loss) and postoperative outcomes (complications, postoperative hospital stay, and body weight loss at one year after surgery) were evaluated and compared between groups. Operative time was significantly shorter in the TOR group (251 min) than in the AI group (282 min) (p < 0.01). There were no statistically significant differences in operative blood loss, postoperative complications, and hospital stay between the 2 study groups. Body weight loss at one year after surgery was -5.8 kg in the TOR group and -6.5 kg in the AI group, without a statistically significant difference. Completion time for Billroth-I anastomosis was significantly shorter with Tornado anastomosis than with the Anterior Incision method, with safety equal between the two methods.

Bilinear modeling of EMG signals to extract user-independent features for multiuser myoelectric interface.

PubMed

Matsubara, Takamitsu; Morimoto, Jun

2013-08-01

In this study, we propose a multiuser myoelectric interface that can easily adapt to novel users. When a user performs different motions (e.g., grasping and pinching), different electromyography (EMG) signals are measured. When different users perform the same motion (e.g., grasping), different EMG signals are also measured. Therefore, designing a myoelectric interface that can be used by multiple users to perform multiple motions is difficult. To cope with this problem, we propose for EMG signals a bilinear model that is composed of two linear factors: 1) user dependent and 2) motion dependent. By decomposing the EMG signals into these two factors, the extracted motion-dependent factors can be used as user-independent features. We can construct a motion classifier on the extracted feature space to develop the multiuser interface. For novel users, the proposed adaptation method estimates the user-dependent factor through only a few interactions. The bilinear EMG model with the estimated user-dependent factor can extract the user-independent features from the novel user data. We applied our proposed method to a recognition task of five hand gestures for robotic hand control using four-channel EMG signals measured from subject forearms. Our method resulted in 73% accuracy, which was statistically significantly different from the accuracy of standard nonmultiuser interfaces, as the result of a two-sample t -test at a significance level of 1%.

Statistical mechanics of unsupervised feature learning in a restricted Boltzmann machine with binary synapses

NASA Astrophysics Data System (ADS)

Huang, Haiping

2017-05-01

Revealing hidden features in unlabeled data is called unsupervised feature learning, which plays an important role in pretraining a deep neural network. Here we provide a statistical mechanics analysis of the unsupervised learning in a restricted Boltzmann machine with binary synapses. A message passing equation to infer the hidden feature is derived, and furthermore, variants of this equation are analyzed. A statistical analysis by replica theory describes the thermodynamic properties of the model. Our analysis confirms an entropy crisis preceding the non-convergence of the message passing equation, suggesting a discontinuous phase transition as a key characteristic of the restricted Boltzmann machine. Continuous phase transition is also confirmed depending on the embedded feature strength in the data. The mean-field result under the replica symmetric assumption agrees with that obtained by running message passing algorithms on single instances of finite sizes. Interestingly, in an approximate Hopfield model, the entropy crisis is absent, and a continuous phase transition is observed instead. We also develop an iterative equation to infer the hyper-parameter (temperature) hidden in the data, which in physics corresponds to iteratively imposing Nishimori condition. Our study provides insights towards understanding the thermodynamic properties of the restricted Boltzmann machine learning, and moreover important theoretical basis to build simplified deep networks.

Support Vector Feature Selection for Early Detection of Anastomosis Leakage From Bag-of-Words in Electronic Health Records.

PubMed

Soguero-Ruiz, Cristina; Hindberg, Kristian; Rojo-Alvarez, Jose Luis; Skrovseth, Stein Olav; Godtliebsen, Fred; Mortensen, Kim; Revhaug, Arthur; Lindsetmo, Rolv-Ole; Augestad, Knut Magne; Jenssen, Robert

2016-09-01

The free text in electronic health records (EHRs) conveys a huge amount of clinical information about health state and patient history. Despite a rapidly growing literature on the use of machine learning techniques for extracting this information, little effort has been invested toward feature selection and the features' corresponding medical interpretation. In this study, we focus on the task of early detection of anastomosis leakage (AL), a severe complication after elective surgery for colorectal cancer (CRC) surgery, using free text extracted from EHRs. We use a bag-of-words model to investigate the potential for feature selection strategies. The purpose is earlier detection of AL and prediction of AL with data generated in the EHR before the actual complication occur. Due to the high dimensionality of the data, we derive feature selection strategies using the robust support vector machine linear maximum margin classifier, by investigating: 1) a simple statistical criterion (leave-one-out-based test); 2) an intensive-computation statistical criterion (Bootstrap resampling); and 3) an advanced statistical criterion (kernel entropy). Results reveal a discriminatory power for early detection of complications after CRC (sensitivity 100%; specificity 72%). These results can be used to develop prediction models, based on EHR data, that can support surgeons and patients in the preoperative decision making phase.

Enhanced echolocation via robust statistics and super-resolution of sonar images

NASA Astrophysics Data System (ADS)

Kim, Kio

Echolocation is a process in which an animal uses acoustic signals to exchange information with environments. In a recent study, Neretti et al. have shown that the use of robust statistics can significantly improve the resiliency of echolocation against noise and enhance its accuracy by suppressing the development of sidelobes in the processing of an echo signal. In this research, the use of robust statistics is extended to problems in underwater explorations. The dissertation consists of two parts. Part I describes how robust statistics can enhance the identification of target objects, which in this case are cylindrical containers filled with four different liquids. Particularly, this work employs a variation of an existing robust estimator called an L-estimator, which was first suggested by Koenker and Bassett. As pointed out by Au et al.; a 'highlight interval' is an important feature, and it is closely related with many other important features that are known to be crucial for dolphin echolocation. A varied L-estimator described in this text is used to enhance the detection of highlight intervals, which eventually leads to a successful classification of echo signals. Part II extends the problem into 2 dimensions. Thanks to the advances in material and computer technology, various sonar imaging modalities are available on the market. By registering acoustic images from such video sequences, one can extract more information on the region of interest. Computer vision and image processing allowed application of robust statistics to the acoustic images produced by forward looking sonar systems, such as Dual-frequency Identification Sonar and ProViewer. The first use of robust statistics for sonar image enhancement in this text is in image registration. Random Sampling Consensus (RANSAC) is widely used for image registration. The registration algorithm using RANSAC is optimized for sonar image registration, and the performance is studied. The second use of robust statistics is in fusing the images. It is shown that the maximum a posteriori fusion method can be formulated in a Kalman filter-like manner, and also that the resulting expression is identical to a W-estimator with a specific weight function.

Effectiveness of feature and classifier algorithms in character recognition systems

NASA Astrophysics Data System (ADS)

Wilson, Charles L.

1993-04-01

At the first Census Optical Character Recognition Systems Conference, NIST generated accuracy data for more than character recognition systems. Most systems were tested on the recognition of isolated digits and upper and lower case alphabetic characters. The recognition experiments were performed on sample sizes of 58,000 digits, and 12,000 upper and lower case alphabetic characters. The algorithms used by the 26 conference participants included rule-based methods, image-based methods, statistical methods, and neural networks. The neural network methods included Multi-Layer Perceptron's, Learned Vector Quantitization, Neocognitrons, and cascaded neural networks. In this paper 11 different systems are compared using correlations between the answers of different systems, comparing the decrease in error rate as a function of confidence of recognition, and comparing the writer dependence of recognition. This comparison shows that methods that used different algorithms for feature extraction and recognition performed with very high levels of correlation. This is true for neural network systems, hybrid systems, and statistically based systems, and leads to the conclusion that neural networks have not yet demonstrated a clear superiority to more conventional statistical methods. Comparison of these results with the models of Vapnick (for estimation problems), MacKay (for Bayesian statistical models), Moody (for effective parameterization), and Boltzmann models (for information content) demonstrate that as the limits of training data variance are approached, all classifier systems have similar statistical properties. The limiting condition can only be approached for sufficiently rich feature sets because the accuracy limit is controlled by the available information content of the training set, which must pass through the feature extraction process prior to classification.

Dynamic Encoding of Speech Sequence Probability in Human Temporal Cortex

PubMed Central

Leonard, Matthew K.; Bouchard, Kristofer E.; Tang, Claire

2015-01-01

Sensory processing involves identification of stimulus features, but also integration with the surrounding sensory and cognitive context. Previous work in animals and humans has shown fine-scale sensitivity to context in the form of learned knowledge about the statistics of the sensory environment, including relative probabilities of discrete units in a stream of sequential auditory input. These statistics are a defining characteristic of one of the most important sequential signals humans encounter: speech. For speech, extensive exposure to a language tunes listeners to the statistics of sound sequences. To address how speech sequence statistics are neurally encoded, we used high-resolution direct cortical recordings from human lateral superior temporal cortex as subjects listened to words and nonwords with varying transition probabilities between sound segments. In addition to their sensitivity to acoustic features (including contextual features, such as coarticulation), we found that neural responses dynamically encoded the language-level probability of both preceding and upcoming speech sounds. Transition probability first negatively modulated neural responses, followed by positive modulation of neural responses, consistent with coordinated predictive and retrospective recognition processes, respectively. Furthermore, transition probability encoding was different for real English words compared with nonwords, providing evidence for online interactions with high-order linguistic knowledge. These results demonstrate that sensory processing of deeply learned stimuli involves integrating physical stimulus features with their contextual sequential structure. Despite not being consciously aware of phoneme sequence statistics, listeners use this information to process spoken input and to link low-level acoustic representations with linguistic information about word identity and meaning. PMID:25948269

TU-CD-BRB-08: Radiomic Analysis of FDG-PET Identifies Novel Prognostic Imaging Biomarkers in Locally Advanced Pancreatic Cancer Patients Treated with SBRT

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

Cui, Y; Shirato, H; Song, J

2015-06-15

Purpose: This study aims to identify novel prognostic imaging biomarkers in locally advanced pancreatic cancer (LAPC) using quantitative, high-throughput image analysis. Methods: 86 patients with LAPC receiving chemotherapy followed by SBRT were retrospectively studied. All patients had a baseline FDG-PET scan prior to SBRT. For each patient, we extracted 435 PET imaging features of five types: statistical, morphological, textural, histogram, and wavelet. These features went through redundancy checks, robustness analysis, as well as a prescreening process based on their concordance indices with respect to the relevant outcomes. We then performed principle component analysis on the remaining features (number ranged frommore » 10 to 16), and fitted a Cox proportional hazard regression model using the first 3 principle components. Kaplan-Meier analysis was used to assess the ability to distinguish high versus low-risk patients separated by median predicted survival. To avoid overfitting, all evaluations were based on leave-one-out cross validation (LOOCV), in which each holdout patient was assigned to a risk group according to the model obtained from a separate training set. Results: For predicting overall survival (OS), the most dominant imaging features were wavelet coefficients. There was a statistically significant difference in OS between patients with predicted high and low-risk based on LOOCV (hazard ratio: 2.26, p<0.001). Similar imaging features were also strongly associated with local progression-free survival (LPFS) (hazard ratio: 1.53, p=0.026) on LOOCV. In comparison, neither SUVmax nor TLG was associated with LPFS (p=0.103, p=0.433) (Table 1). Results for progression-free survival and distant progression-free survival showed similar trends. Conclusion: Radiomic analysis identified novel imaging features that showed improved prognostic value over conventional methods. These features characterize the degree of intra-tumor heterogeneity reflected on FDG-PET images, and their biological underpinnings warrant further investigation. If validated in large, prospective cohorts, this method could be used to stratify patients based on individualized risk.« less

Hereditary factors are unlikely behind unusual pattern of early - Onset colorectal cancer in Egyptians: A study of family history and pathology features in Egyptians with large bowel cancer (cross-sectional study).

PubMed

Abou-Zeid, Ahmed A; Jumuah, Wael A; Ebied, Essam F; Abd El Samee Atia, Karim Sabry; El Ghamrini, Yasser; Somaie, Dina A

2017-08-01

Colorectal cancer in Egypt has a higher incidence in young patients compared to western countries, where the disease is more prevalent in the old age group. This difference has been attributed to higher incidence of hereditary cancers in young Egyptian patients. The aim of this study is to compare the family history criteria and pathology features of tumors in young (≤40 years) and old (>40 years) Egyptian patients with adenocarcinoma of the colon and rectum. This is the analysis of our prospectively collected data on the pathology features of tumors in 313 consecutive patients (133 young, 180 old) with colorectal cancer presenting to the Department of Surgery within an eight-year period. A detailed family history was obtained from 258 patients (112 young, 146 old). 41 young and 48 old patients reported family history of cancer, the difference was not statistically significant. Ten young patients (9%) reported a family history of colorectal cancer in a first degree relative (3 fitting into Amsterdam criteria, 7 fitting into less strict criteria) which was not significantly different from the old age group. The pathologic features of tumors in both groups resembled sporadic rather than hereditary cancer and there was no significant difference between groups in tumor location, degree of differentiation, mucin production, synchronous and metachronous colorectal tumors or polyps and grossly stricturing or ulcerating tumors. Extracolonic tumors developed in one young and two old patients. The characteristics of large bowel cancer in young Egyptian patients do not differ significantly from those in older patients. Despite the high incidence of large bowel cancer in young Egyptian patients, family history and pathologic features of tumors do not support a hereditary origin of colorectal cancer in this age group in Egypt. Copyright © 2017 IJS Publishing Group Ltd. Published by Elsevier Ltd. All rights reserved.

Analysis of cerebral vessels dynamics using experimental data with missed segments

NASA Astrophysics Data System (ADS)

Pavlova, O. N.; Abdurashitov, A. S.; Ulanova, M. V.; Shihalov, G. M.; Semyachkina-Glushkovskaya, O. V.; Pavlov, A. N.

2018-04-01

Physiological signals often contain various bad segments that occur due to artifacts, failures of the recording equipment or varying experimental conditions. The related experimental data need to be preprocessed to avoid such parts of recordings. In the case of few bad segments, they can simply be removed from the signal and its analysis is further performed. However, when there are many extracted segments, the internal structure of the analyzed physiological process may be destroyed, and it is unclear whether such signal can be used in diagnostic-related studies. In this paper we address this problem for the case of cerebral vessels dynamics. We perform analysis of simulated data in order to reveal general features of quantifying scaling features of complex signals with distinct correlation properties and show that the effects of data loss are significantly different for experimental data with long-range correlations and anti-correlations. We conclude that the cerebral vessels dynamics is significantly less sensitive to missed data fragments as compared with signals with anti-correlated statistics.

Single- and double-row repair for rotator cuff tears - biology and mechanics.

PubMed

Papalia, Rocco; Franceschi, Francesco; Vasta, Sebastiano; Zampogna, Biagio; Maffulli, Nicola; Denaro, Vincenzo

2012-01-01

We critically review the existing studies comparing the features of single- and double-row repair, and discuss suggestions about the surgical indications for the two repair techniques. All currently available studies comparing the biomechanical, clinical and the biological features of single and double row. Biomechanically, the double-row repair has greater performances in terms of higher initial fixation strength, greater footprint coverage, improved contact area and pressure, decreased gap formation, and higher load to failure. Results of clinical studies demonstrate no significantly better outcomes for double-row compared to single-row repair. Better results are achieved by double-row repair for larger lesions (tear size 2.5-3.5 cm). Considering the lack of statistically significant differences between the two techniques and that the double row is a high cost and a high surgical skill-dependent technique, we suggest using the double-row technique only in strictly selected patients. Copyright © 2012 S. Karger AG, Basel.

Comparing Pattern Recognition Feature Sets for Sorting Triples in the FIRST Database

NASA Astrophysics Data System (ADS)

Proctor, D. D.

2006-07-01

Pattern recognition techniques have been used with increasing success for coping with the tremendous amounts of data being generated by automated surveys. Usually this process involves construction of training sets, the typical examples of data with known classifications. Given a feature set, along with the training set, statistical methods can be employed to generate a classifier. The classifier is then applied to process the remaining data. Feature set selection, however, is still an issue. This paper presents techniques developed for accommodating data for which a substantive portion of the training set cannot be classified unambiguously, a typical case for low-resolution data. Significance tests on the sort-ordered, sample-size-normalized vote distribution of an ensemble of decision trees is introduced as a method of evaluating relative quality of feature sets. The technique is applied to comparing feature sets for sorting a particular radio galaxy morphology, bent-doubles, from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) database. Also examined are alternative functional forms for feature sets. Associated standard deviations provide the means to evaluate the effect of the number of folds, the number of classifiers per fold, and the sample size on the resulting classifications. The technique also may be applied to situations for which, although accurate classifications are available, the feature set is clearly inadequate, but is desired nonetheless to make the best of available information.

qFeature

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

2015-09-14

This package contains statistical routines for extracting features from multivariate time-series data which can then be used for subsequent multivariate statistical analysis to identify patterns and anomalous behavior. It calculates local linear or quadratic regression model fits to moving windows for each series and then summarizes the model coefficients across user-defined time intervals for each series. These methods are domain agnostic-but they have been successfully applied to a variety of domains, including commercial aviation and electric power grid data.

Incidental prostate cancer at the time of cystectomy: the incidence and clinicopathological features in Chinese patients.

PubMed

Pan, Jiahua; Xue, Wei; Sha, Jianjun; Yang, Hu; Xu, Fan; Xuan, Hanqing; Li, Dong; Huang, Yiran

2014-01-01

To evaluate the incidence and the clinicopathological features of incidental prostate cancer detected in radical cystoprostatectomy (RCP) specimens in Chinese men and to estimate the oncological risk of prostate apex-sparing surgery for such patients. The clinical data and pathological feature of 504 patients who underwent RCP for bladder cancer from January 1999 to March 2013 were retrospectively reviewed. Whole mount serial section of the RCP specimens were cut transversely at 3-4 mm intervals and examined in same pathological institution. Thirty-four out of 504 patients (6.8%) had incidental prostate cancer with a mean age of 70.3 years. 12 cases (35.2%) were diagnosed as significant disease. 4 cases were found to have apex involvement of adenocarcinoma of the prostate while in 5 cases the prostate stroma invasion by urothelial carcinoma were identified (one involved prostate apex). The mean follow-up time was 46.4±33.8 months. Biochemical recurrence occurred in 3 patients but no prostate cancer-related death during the follow-up. There was no statistical significance in cancer specific survival between the clinically significant and insignificant cancer group. The prevalence of incidental prostate cancer in RCP specimens in Chinese patients was remarkably lower than in western people. Most of the incidental prostate cancer was clinically insignificant and patient's prognosis was mainly related to the bladder cancer. Sparing the prostate apex was potentially associated with a 1.0% risk of leaving significant cancer of the prostate or urothelial carcinoma.

EEG-based Affect and Workload Recognition in a Virtual Driving Environment for ASD Intervention

PubMed Central

Wade, Joshua W.; Key, Alexandra P.; Warren, Zachary E.; Sarkar, Nilanjan

2017-01-01

objective To build group-level classification models capable of recognizing affective states and mental workload of individuals with autism spectrum disorder (ASD) during driving skill training. Methods Twenty adolescents with ASD participated in a six-session virtual reality driving simulator based experiment, during which their electroencephalogram (EEG) data were recorded alongside driving events and a therapist’s rating of their affective states and mental workload. Five feature generation approaches including statistical features, fractal dimension features, higher order crossings (HOC)-based features, power features from frequency bands, and power features from bins (Δf = 2 Hz) were applied to extract relevant features. Individual differences were removed with a two-step feature calibration method. Finally, binary classification results based on the k-nearest neighbors algorithm and univariate feature selection method were evaluated by leave-one-subject-out nested cross-validation to compare feature types and identify discriminative features. Results The best classification results were achieved using power features from bins for engagement (0.95) and boredom (0.78), and HOC-based features for enjoyment (0.90), frustration (0.88), and workload (0.86). Conclusion Offline EEG-based group-level classification models are feasible for recognizing binary low and high intensity of affect and workload of individuals with ASD in the context of driving. However, while promising the applicability of the models in an online adaptive driving task requires further development. Significance The developed models provide a basis for an EEG-based passive brain computer interface system that has the potential to benefit individuals with ASD with an affect- and workload-based individualized driving skill training intervention. PMID:28422647

Spatio-temporal surveillance of water based infectious disease (malaria) in Rawalpindi, Pakistan using geostatistical modeling techniques.

PubMed

Ahmad, Sheikh Saeed; Aziz, Neelam; Butt, Amna; Shabbir, Rabia; Erum, Summra

2015-09-01

One of the features of medical geography that has made it so useful in health research is statistical spatial analysis, which enables the quantification and qualification of health events. The main objective of this research was to study the spatial distribution patterns of malaria in Rawalpindi district using spatial statistical techniques to identify the hot spots and the possible risk factor. Spatial statistical analyses were done in ArcGIS, and satellite images for land use classification were processed in ERDAS Imagine. Four hundred and fifty water samples were also collected from the study area to identify the presence or absence of any microbial contamination. The results of this study indicated that malaria incidence varied according to geographical location, with eco-climatic condition and showing significant positive spatial autocorrelation. Hotspots or location of clusters were identified using Getis-Ord Gi* statistic. Significant clustering of malaria incidence occurred in rural central part of the study area including Gujar Khan, Kaller Syedan, and some part of Kahuta and Rawalpindi Tehsil. Ordinary least square (OLS) regression analysis was conducted to analyze the relationship of risk factors with the disease cases. Relationship of different land cover with the disease cases indicated that malaria was more related with agriculture, low vegetation, and water class. Temporal variation of malaria cases showed significant positive association with the meteorological variables including average monthly rainfall and temperature. The results of the study further suggested that water supply and sewage system and solid waste collection system needs a serious attention to prevent any outbreak in the study area.

Parallel object-oriented data mining system

DOEpatents

Kamath, Chandrika; Cantu-Paz, Erick

2004-01-06

A data mining system uncovers patterns, associations, anomalies and other statistically significant structures in data. Data files are read and displayed. Objects in the data files are identified. Relevant features for the objects are extracted. Patterns among the objects are recognized based upon the features. Data from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) sky survey was used to search for bent doubles. This test was conducted on data from the Very Large Array in New Mexico which seeks to locate a special type of quasar (radio-emitting stellar object) called bent doubles. The FIRST survey has generated more than 32,000 images of the sky to date. Each image is 7.1 megabytes, yielding more than 100 gigabytes of image data in the entire data set.

Protein Aggregation/Folding: The Role of Deterministic Singularities of Sequence Hydrophobicity as Determined by Nonlinear Signal Analysis of Acylphosphatase and Aβ(1–40)

PubMed Central

Zbilut, Joseph P.; Colosimo, Alfredo; Conti, Filippo; Colafranceschi, Mauro; Manetti, Cesare; Valerio, MariaCristina; Webber, Charles L.; Giuliani, Alessandro

2003-01-01

The problem of protein folding vs. aggregation was investigated in acylphosphatase and the amyloid protein Aβ(1–40) by means of nonlinear signal analysis of their chain hydrophobicity. Numerical descriptors of recurrence patterns provided the basis for statistical evaluation of folding/aggregation distinctive features. Static and dynamic approaches were used to elucidate conditions coincident with folding vs. aggregation using comparisons with known protein secondary structure classifications, site-directed mutagenesis studies of acylphosphatase, and molecular dynamics simulations of amyloid protein, Aβ(1–40). The results suggest that a feature derived from principal component space characterized by the smoothness of singular, deterministic hydrophobicity patches plays a significant role in the conditions governing protein aggregation. PMID:14645049

Inhomogeneous kinetic effects related to intermittent magnetic discontinuities

NASA Astrophysics Data System (ADS)

Greco, A.; Valentini, F.; Servidio, S.; Matthaeus, W. H.

2012-12-01

A connection between kinetic processes and two-dimensional intermittent plasma turbulence is observed using direct numerical simulations of a hybrid Vlasov-Maxwell model, in which the Vlasov equation is solved for protons, while the electrons are described as a massless fluid. During the development of turbulence, the proton distribution functions depart from the typical configuration of local thermodynamic equilibrium, displaying statistically significant non-Maxwellian features. In particular, temperature anisotropy and distortions are concentrated near coherent structures, generated as the result of the turbulent cascade, such as current sheets, which are nonuniformly distributed in space. Here, the partial variance of increments (PVI) method has been employed to identify high magnetic stress regions within a two-dimensional turbulent pattern. A quantitative association between non-Maxwellian features and coherent structures is established.

A wavelet-based technique to predict treatment outcome for Major Depressive Disorder.

PubMed

Mumtaz, Wajid; Xia, Likun; Mohd Yasin, Mohd Azhar; Azhar Ali, Syed Saad; Malik, Aamir Saeed

2017-01-01

Treatment management for Major Depressive Disorder (MDD) has been challenging. However, electroencephalogram (EEG)-based predictions of antidepressant's treatment outcome may help during antidepressant's selection and ultimately improve the quality of life for MDD patients. In this study, a machine learning (ML) method involving pretreatment EEG data was proposed to perform such predictions for Selective Serotonin Reuptake Inhibitor (SSRIs). For this purpose, the acquisition of experimental data involved 34 MDD patients and 30 healthy controls. Consequently, a feature matrix was constructed involving time-frequency decomposition of EEG data based on wavelet transform (WT) analysis, termed as EEG data matrix. However, the resultant EEG data matrix had high dimensionality. Therefore, dimension reduction was performed based on a rank-based feature selection method according to a criterion, i.e., receiver operating characteristic (ROC). As a result, the most significant features were identified and further be utilized during the training and testing of a classification model, i.e., the logistic regression (LR) classifier. Finally, the LR model was validated with 100 iterations of 10-fold cross-validation (10-CV). The classification results were compared with short-time Fourier transform (STFT) analysis, and empirical mode decompositions (EMD). The wavelet features extracted from frontal and temporal EEG data were found statistically significant. In comparison with other time-frequency approaches such as the STFT and EMD, the WT analysis has shown highest classification accuracy, i.e., accuracy = 87.5%, sensitivity = 95%, and specificity = 80%. In conclusion, significant wavelet coefficients extracted from frontal and temporal pre-treatment EEG data involving delta and theta frequency bands may predict antidepressant's treatment outcome for the MDD patients.

Searching for hidden unexpected features in the SnIa data

NASA Astrophysics Data System (ADS)

Shafieloo, A.; Perivolaropoulos, L.

2010-06-01

It is known that κ2 statistic and likelihood analysis may not be sensitive to the all features of the data. Despite of the fact that by using κ2 statistic we can measure the overall goodness of fit for a model confronted to a data set, some specific features of the data can stay undetectable. For instance, it has been pointed out that there is an unexpected brightness of the SnIa data at z > 1 in the Union compilation. We quantify this statement by constructing a new statistic, called Binned Normalized Difference (BND) statistic, which is applicable directly on the Type Ia Supernova (SnIa) distance moduli. This statistic is designed to pick up systematic brightness trends of SnIa data points with respect to a best fit cosmological model at high redshifts. According to this statistic there are 2.2%, 5.3% and 12.6% consistency between the Gold06, Union08 and Constitution09 data and spatially flat ΛCDM model when the real data is compared with many realizations of the simulated monte carlo datasets. The corresponding realization probability in the context of a (w0,w1) = (-1.4,2) model is more than 30% for all mentioned datasets indicating a much better consistency for this model with respect to the BND statistic. The unexpected high z brightness of SnIa can be interpreted either as a trend towards more deceleration at high z than expected in the context of ΛCDM or as a statistical fluctuation or finally as a systematic effect perhaps due to a mild SnIa evolution at high z.