A novel MAP-MRF approach for multispectral image contextual classification using combination of suboptimal iterative algorithms

In this paper we present a novel approach for multispectral image contextual classification by combining iterative combinatorial optimization algorithms. The pixel-wise decision rule is defined using a Bayesian approach to combine two MRF models: a Gaussian Markov Random Field (GMRF) for the observations (likelihood) and a Potts model for the a priori knowledge, to regularize the solution in the presence of noisy data. Hence, the classification problem is stated according to a Maximum a Posteriori (MAP) framework. In order to approximate the MAP solution we apply several combinatorial optimization methods using multiple simultaneous initializations, making the solution less sensitive to the initial conditions and reducing both computational cost and time in comparison to Simulated Annealing, often unfeasible in many real image processing applications. Markov Random Field model parameters are estimated by Maximum Pseudo-Likelihood (MPL) approach, avoiding manual adjustments in the choice of the regularization parameters. Asymptotic evaluations assess the accuracy of the proposed parameter estimation procedure. To test and evaluate the proposed classification method, we adopt metrics for quantitative performance assessment (Cohen`s Kappa coefficient), allowing a robust and accurate statistical analysis. The obtained results clearly show that combining sub-optimal contextual algorithms significantly improves the classification performance, indicating the effectiveness of the proposed methodology. (C) 2010 Elsevier B.V. All rights reserved.

Detecção de áreas degradadas na sub-bacia hidrográfica do rio taperoá/pb, utilizando parâmetros físicos dos sensores modis/terra e tm/landsat

This study includes the results of the analysis of areas susceptible to degradation by remote sensing in semi-arid region, which is a matter of concern and affects the whole population and the catalyst of this process occurs by the deforestation of the savanna and improper practices by the use of soil. The objective of this research is to use biophysical parameters of the MODIS / Terra and images TM/Landsat-5 to determine areas susceptible to degradation in semi-arid Paraiba. The study area is located in the central interior of Paraíba, in the sub-basin of the River Taperoá, with average annual rainfall below 400 mm and average annual temperature of 28 ° C. To draw up the map of vegetation were used TM/Landsat-5 images, specifically, the composition 5R4G3B colored, commonly used for mapping land use. This map was produced by unsupervised classification by maximum likelihood. The legend corresponds to the following targets: savanna vegetation sparse and dense, riparian vegetation and exposed soil. The biophysical parameters used in the MODIS were emissivity, albedo and vegetation index for NDVI (NDVI). The GIS computer programs used were Modis Reprojections Tools and System Information Processing Georeferenced (SPRING), which was set up and worked the bank of information from sensors MODIS and TM and ArcGIS software for making maps more customizable. Initially, we evaluated the behavior of the vegetation emissivity by adapting equation Bastiaanssen on NDVI for spatialize emissivity and observe changes during the year 2006. The albedo was used to view your percentage of increase in the periods December 2003 and 2004. The image sensor of Landsat TM were used for the month of December 2005, according to the availability of images and in periods of low emissivity. For these applications were made in language programs for GIS Algebraic Space (LEGAL), which is a routine programming SPRING, which allows you to perform various types of algebras of spatial data and maps. For the detection of areas susceptible to environmental degradation took into account the behavior of the emissivity of the savanna that showed seasonal coinciding with the rainy season, reaching a maximum emissivity in the months April to July and in the remaining months of a low emissivity . With the images of the albedo of December 2003 and 2004, it was verified the percentage increase, which allowed the generation of two distinct classes: areas with increased variation percentage of 1 to 11.6% and the percentage change in areas with less than 1 % albedo. It was then possible to generate the map of susceptibility to environmental degradation, with the intersection of the class of exposed soil with varying percentage of the albedo, resulting in classes susceptibility to environmental degradation

Two-way regionalized classification of multivariate datasets and its application to the assessment of hydrodynamic dispersion

Zones of mixing between shallow groundwaters of different composition were unravelled by two-way regionalized classification, a technique based on correspondence analysis (CA), cluster analysis (ClA) and discriminant analysis (DA), aided by gridding, map-overlay and contouring tools. The shallow groundwaters are from a granitoid plutonite in the Funda o region (central Portugal). Correspondence analysis detected three natural clusters in the working dataset: 1, weathering; 2, domestic effluents; 3, fertilizers. Cluster analysis set an alternative distribution of the samples by the three clusters. Group memberships obtained by correspondence analysis and by cluster analysis were optimized by discriminant analysis, gridded memberships as follows: codes 1, 2 or 3 were used when classification by correspondence analysis and cluster analysis produced the same results; code 0 when the grid node was first assigned to cluster 1 and then to cluster 2 or vice versa (mixing between weathering and effluents); code 4 in the other cases (mixing between agriculture and the other influences). Code-3 areas were systematically surrounded by code-4 areas, an observation attributed to hydrodynamic dispersion. Accordingly, the extent of code-4 areas in two orthogonal directions was assumed proportional to the longitudinal and transverse dispersivities of local soils. The results (0.7-16.8 and 0.4-4.3 m, respectively) are acceptable at the macroscopic scale. The ratios between longitudinal and transverse dispersivities (1.2-11.1) are also in agreement with results obtained by other studies.

Toward Satellite-Based Land Cover Classification Through Optimum-Path Forest

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

3D MPRAGE improves classification of cortical lesions in multiple sclerosis.

BACKGROUND: Gray matter lesions are known to be common in multiple sclerosis (MS) and are suspected to play an important role in disease progression and clinical disability. A combination of magnetic resonance imaging (MRI) techniques, double-inversion recovery (DIR), and phase-sensitive inversion recovery (PSIR), has been used for detection and classification of cortical lesions. This study shows that high-resolution three-dimensional (3D) magnetization-prepared rapid acquisition with gradient echo (MPRAGE) improves the classification of cortical lesions by allowing more accurate anatomic localization of lesion morphology. METHODS: 11 patients with MS with previously identified cortical lesions were scanned using DIR, PSIR, and 3D MPRAGE. Lesions were identified on DIR and PSIR and classified as purely intracortical or mixed. MPRAGE images were then examined, and lesions were re-classified based on the new information. RESULTS: The high signal-to-noise ratio, fine anatomic detail, and clear gray-white matter tissue contrast seen in the MPRAGE images provided superior delineation of lesion borders and surrounding gray-white matter junction, improving classification accuracy. 119 lesions were identified as either intracortical or mixed on DIR/PSIR. In 89 cases, MPRAGE confirmed the classification by DIR/PSIR. In 30 cases, MPRAGE overturned the original classification. CONCLUSION: Improved classification of cortical lesions was realized by inclusion of high-spatial resolution 3D MPRAGE. This sequence provides unique detail on lesion morphology that is necessary for accurate classification.

Performance tiers: Implementing comparative effectiveness analysis in the health care setting through operations research and spatial methods

This dissertation develops and tests a comparative effectiveness methodology utilizing a novel approach to the application of Data Envelopment Analysis (DEA) in health studies. The concept of performance tiers (PerT) is introduced as terminology to express a relative risk class for individuals within a peer group and the PerT calculation is implemented with operations research (DEA) and spatial algorithms. The analysis results in the discrimination of the individual data observations into a relative risk classification by the DEA-PerT methodology. The performance of two distance measures, kNN (k-nearest neighbor) and Mahalanobis, was subsequently tested to classify new entrants into the appropriate tier. The methods were applied to subject data for the 14 year old cohort in the Project HeartBeat! study.^ The concepts presented herein represent a paradigm shift in the potential for public health applications to identify and respond to individual health status. The resultant classification scheme provides descriptive, and potentially prescriptive, guidance to assess and implement treatments and strategies to improve the delivery and performance of health systems. ^

INTEGRATIVE BIOMARKER IDENTIFICATION AND CLASSIFICATION USING HIGH THROUGHPUT ASSAYS

It is well accepted that tumorigenesis is a multi-step procedure involving aberrant functioning of genes regulating cell proliferation, differentiation, apoptosis, genome stability, angiogenesis and motility. To obtain a full understanding of tumorigenesis, it is necessary to collect information on all aspects of cell activity. Recent advances in high throughput technologies allow biologists to generate massive amounts of data, more than might have been imagined decades ago. These advances have made it possible to launch comprehensive projects such as (TCGA) and (ICGC) which systematically characterize the molecular fingerprints of cancer cells using gene expression, methylation, copy number, microRNA and SNP microarrays as well as next generation sequencing assays interrogating somatic mutation, insertion, deletion, translocation and structural rearrangements. Given the massive amount of data, a major challenge is to integrate information from multiple sources and formulate testable hypotheses. This thesis focuses on developing methodologies for integrative analyses of genomic assays profiled on the same set of samples. We have developed several novel methods for integrative biomarker identification and cancer classification. We introduce a regression-based approach to identify biomarkers predictive to therapy response or survival by integrating multiple assays including gene expression, methylation and copy number data through penalized regression. To identify key cancer-specific genes accounting for multiple mechanisms of regulation, we have developed the integIRTy software that provides robust and reliable inferences about gene alteration by automatically adjusting for sample heterogeneity as well as technical artifacts using Item Response Theory. To cope with the increasing need for accurate cancer diagnosis and individualized therapy, we have developed a robust and powerful algorithm called SIBER to systematically identify bimodally expressed genes using next generation RNAseq data. We have shown that prediction models built from these bimodal genes have the same accuracy as models built from all genes. Further, prediction models with dichotomized gene expression measurements based on their bimodal shapes still perform well. The effectiveness of outcome prediction using discretized signals paves the road for more accurate and interpretable cancer classification by integrating signals from multiple sources.

Improving kNN multi-label classification in Prototype Selection scenarios using class proposals

Prototype Selection (PS) algorithms allow a faster Nearest Neighbor classification by keeping only the most profitable prototypes of the training set. In turn, these schemes typically lower the performance accuracy. In this work a new strategy for multi-label classifications tasks is proposed to solve this accuracy drop without the need of using all the training set. For that, given a new instance, the PS algorithm is used as a fast recommender system which retrieves the most likely classes. Then, the actual classification is performed only considering the prototypes from the initial training set belonging to the suggested classes. Results show that this strategy provides a large set of trade-off solutions which fills the gap between PS-based classification efficiency and conventional kNN accuracy. Furthermore, this scheme is not only able to, at best, reach the performance of conventional kNN with barely a third of distances computed, but it does also outperform the latter in noisy scenarios, proving to be a much more robust approach.

Oat history, identification and classification /

Includes index.

Marine mammal species detection and classification

Thesis (Ph.D.)--University of Washington, 2016-04

USEWRITE: Useful Writing Techniques to Improve High-School Students`Descriptive Writing

Se analizan y recomiendan algunas técnicas de redacción, para promover la escritura de párrafos descriptivos, entre estudiantes de secundaria o principiantes. El estudio parte de los resultados obtenidos en una investigación llevada a cabo en un colegio del distrito de Pérez Zeledón (Costa Rica). Se propone una secuencia de técnicas fundamentadas en el enfoque de escritura equilibrada, el enfoque de escritura guiada, la redacción como proceso, las estrategias del aprendizaje, los estilos de aprendizaje y la teoría de inteligencias múltiples. Composition techniques designed to promote the writing of descriptive paragraphs by high school students are analyzed and recommended. This study is based on the results gathered from research conducted in a high school located in the district of Pérez Zeledón (Costa Rica). A proposal of a sequence of techniques has been developed, applying the Balanced Approach, the Guided Writing Approach, Process Approach, Leaming Strategies, Leaming Styles, and Multiple Intelligences Theory.

Genetic Background of Patients from a University Medical Center in Manhattan: Implications for Personalized Medicine

Background: The rapid progress currently being made in genomic science has created interest in potential clinical applications; however, formal translational research has been limited thus far. Studies of population genetics have demonstrated substantial variation in allele frequencies and haplotype structure at loci of medical relevance and the genetic background of patient cohorts may often be complex. Methods and Findings: To describe the heterogeneity in an unselected clinical sample we used the Affymetrix 6.0 gene array chip to genotype self-identified European Americans (N = 326), African Americans (N = 324) and Hispanics (N = 327) from the medical practice of Mount Sinai Medical Center in Manhattan, NY. Additional data from US minority groups and Brazil were used for external comparison. Substantial variation in ancestral origin was observed for both African Americans and Hispanics; data from the latter group overlapped with both Mexican Americans and Brazilians in the external data sets. A pooled analysis of the African Americans and Hispanics from NY demonstrated a broad continuum of ancestral origin making classification by race/ethnicity uninformative. Selected loci harboring variants associated with medical traits and drug response confirmed substantial within-and between-group heterogeneity. Conclusion: As a consequence of these complementary levels of heterogeneity group labels offered no guidance at the individual level. These findings demonstrate the complexity involved in clinical translation of the results from genome-wide association studies and suggest that in the genomic era conventional racial/ethnic labels are of little value.

Validation of body condition score as a predictor of subcutaneous fat in Nelore (Bos indicus) cows

The aim of the present study was to determine the relationship among body weight (BW), body condition score (BCS) and rump fat thickness (RFAT) measured by ultrasonography, and validate the relationship between BCS and RFAT over the time. Two hundred sixty and six Nelore cows had their BW, BCS and RFAT evaluated at five different moments during the production cycle: M1) weaning: M2) parturition, M3) 42 days post-partum; M4) 82 days postpartum and M5) 112 days post-partum. A BCS value was attributed for each cow following a I to 5 points scale. Ultrasonographic images for RFAT measurement were obtained using a 3.5 MHz linear transducer. Images were immediately analyzed as soon as they were formed and frozen. Body condition scores and ultrasound measurements were collected on the same day by a single trained technician. The relationship between BCS and RFAT values was investigated by regression models. The analysis of similarity among the five obtained models was performed using the proc MIXED from SAS and the correlations among variables were analyzed with proc CORR from SAS. The BCS was able to predict RFAT in Nelore cows in all different moments evaluated. Also, it was shown that BCS presented high correlation (r=0.82 to 0.93) and relationship (R(2) = 0.73 to 0.92) with RFAT. However, both BCS and RFAT showed low correlation (r=0.37 to 0.50) and relationship (R(2) = 0.13 to 0.25) with BW. The BCS classification by visual method using a 1 to 5 point scale, was able to predict the RFAT in Nelore cows over the time. (C) 2008 Elsevier B.V. All rights reserved.

Alcoholic liver disease: A clinical series in an Australian private practice

Background and Aim: The published literature on alcoholic liver disease (ALD) in Australia lacks a large clinical series out of private practice as distinct from hospital-based hepatology referral units. This series describes the presentation and clinical features of ALD in a consecutive series out of metropolitan private practice in Australia. Methods: A retrospective descriptive study by case-note review found 297 cases of ALD at a Brisbane practice over 20 years. The main outcome measures were: clinical features and stage at presentation, reasons for referral, and the predictive value of aspartate aminotransferase (AST)/alanine aminotransferase (ALT) ratio. Results: Most patients (57.9%) had no symptoms of liver disease and 29 patients (9.8%) had neither symptoms nor signs. Cirrhosis was found in 41% of patients and hepatitis-fibrosis was found in 26% of patients. The male to female (M: F) ratio was 4.7:1. The AST/ALT ratio was not reliably predictive of ALD stage. The average reported daily alcohol intake was 131 g. Females drank less on average and presented a more vigorous clinical picture. Conclusions: This series presents the spectrum of ALD in a metropolitan Australian private practice. Many patients are asymptomatic on presentation. All heavy drinkers should be targeted for early investigation without waiting for volunteered symptoms or abnormal physical signs. The male to female ratio in ALD is higher than hitherto reported. The AST/ALT ratio is not generally applicable in the staging of ALD. The differences from hospital series data suggest the demography and epidemiology of ALD in Australia are incomplete, and further study is warranted. (C) 2001 Blackwell Science Asia Pty Ltd.

Linguagens documentais para as bibliotecas escolares: o caso da Espanha, Portugal e Brasil

Este artigo analisa as características específicas e os processos de indexação e classificação realizados em bibliotecas escolares para tratar e recuperar as informações de suas coleções. Também se analisam as linguagens como ferramentas documentais específicas utilizadas em bibliotecas escolares portuguesas espanholas, portuguesas e brasileiras. Para atingir este objetivo, o modelo de biblioteca escolar é estudado de forma crítica, se analisa o conceito de biblioteca escolar de forma crítica, se estudam suas funções e se examinam as técnicas e os instrumentos que permitem organizar a informação. Entre outras ferramentas, estudam-se listas de cabeçalhos de assuntos como os Cabeçalhos de assuntos para livros infantis e juvenis e a Lista de Cabeçalhos de assuntos para as bibliotecas; sistemas de classificação, como a Classificação Decimal Universal (edição de bolso) ou a classificação por centros de interesse e tesauros especializados como o Tesauro da Educação UNESCO-OIE e o Tesauro Europeu da Educação, entre outros.