The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Charles Decarli - One of the best experts on this subject based on the ideXlab platform.
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validation of a Regression Technique for segmentation of white matter hyperintensities in alzheimer s disease
IEEE Transactions on Medical Imaging, 2017Co-Authors: Mahsa Dadar, Tharick A Pascoal, Sarinporn Manitsirikul, Karen Misquitta, Vladimir S Fonov, Carmela M Tartaglia, John C S Breitner, Pedro Rosaneto, Owen Carmichael, Charles DecarliAbstract:Segmentation and volumetric quantification of white matter hyperintensities (WMHs) is essential in assessment and monitoring of the vascular burden in aging and Alzheimer’s disease (AD), especially when considering their effect on cognition. Manually segmenting WMHs in large cohorts is technically unfeasible due to time and accuracy concerns. Automated tools that can detect WMHs robustly and with high accuracy are needed. Here, we present and validate a fully automatic Technique for segmentation and volumetric quantification of WMHs in aging and AD. The proposed Technique combines intensity and location features frommultiplemagnetic resonance imaging contrasts and manually labeled training data with a linear classifier to perform fast and robust segmentations. It provides both a continuous subject specific WMH map reflecting different levels of tissue damage and binary segmentations. Themethodwas used to detectWMHs in 80 elderly/AD brains (ADC data set) as well as 40 healthy subjects at risk of AD (PREVENT-AD data set). Robustness across different scanners was validated using ten subjects from ADNI2/GO study. Voxel-wise and volumetric agreements were evaluated using Dice similarity index (SI) and intra-class correlation (ICC), yielding ${\mathrm{ ICC}}=0.96$ , ${\mathrm{ SI}}= 0.62\pm 0.16$ for ADC data set and ${\mathrm{ ICC}}=0.78$ , ${\mathrm{ SI}}=0.51\pm 0.15$ for PREVENT-AD data set. The proposed method was robust in the independent sample yielding ${\mathrm{ SI}}=0.64\pm 0.17$ with ${\mathrm{ ICC}}=0.93$ for ADNI2/GO subjects. The proposed method provides fast, accurate, and robust segmentations on previously unseen data from different models of scanners, making it ideal to study WMHs in large scale multi-site studies.
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validation of a Regression Technique for segmentation of white matter hyperintensities in alzheimer s disease
IEEE Transactions on Medical Imaging, 2017Co-Authors: Mahsa Dadar, Tharick A Pascoal, Sarinporn Manitsirikul, Karen Misquitta, Vladimir S Fonov, Carmela M Tartaglia, John C S Breitner, Pedro Rosaneto, Owen Carmichael, Charles DecarliAbstract:Segmentation and volumetric quantification of white matter hyperintensities (WMHs) is essential in assessment and monitoring of the vascular burden in aging and Alzheimer’s disease (AD), especially when considering their effect on cognition. Manually segmenting WMHs in large cohorts is technically unfeasible due to time and accuracy concerns. Automated tools that can detect WMHs robustly and with high accuracy are needed. Here, we present and validate a fully automatic Technique for segmentation and volumetric quantification of WMHs in aging and AD. The proposed Technique combines intensity and location features frommultiplemagnetic resonance imaging contrasts and manually labeled training data with a linear classifier to perform fast and robust segmentations. It provides both a continuous subject specific WMH map reflecting different levels of tissue damage and binary segmentations. Themethodwas used to detectWMHs in 80 elderly/AD brains (ADC data set) as well as 40 healthy subjects at risk of AD (PREVENT-AD data set). Robustness across different scanners was validated using ten subjects from ADNI2/GO study. Voxel-wise and volumetric agreements were evaluated using Dice similarity index (SI) and intra-class correlation (ICC), yielding ${\mathrm{ ICC}}=0.96$ , ${\mathrm{ SI}}= 0.62\pm 0.16$ for ADC data set and ${\mathrm{ ICC}}=0.78$ , ${\mathrm{ SI}}=0.51\pm 0.15$ for PREVENT-AD data set. The proposed method was robust in the independent sample yielding ${\mathrm{ SI}}=0.64\pm 0.17$ with ${\mathrm{ ICC}}=0.93$ for ADNI2/GO subjects. The proposed method provides fast, accurate, and robust segmentations on previously unseen data from different models of scanners, making it ideal to study WMHs in large scale multi-site studies.
Mahsa Dadar - One of the best experts on this subject based on the ideXlab platform.
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validation of a Regression Technique for segmentation of white matter hyperintensities in alzheimer s disease
IEEE Transactions on Medical Imaging, 2017Co-Authors: Mahsa Dadar, Tharick A Pascoal, Sarinporn Manitsirikul, Karen Misquitta, Vladimir S Fonov, Carmela M Tartaglia, John C S Breitner, Pedro Rosaneto, Owen Carmichael, Charles DecarliAbstract:Segmentation and volumetric quantification of white matter hyperintensities (WMHs) is essential in assessment and monitoring of the vascular burden in aging and Alzheimer’s disease (AD), especially when considering their effect on cognition. Manually segmenting WMHs in large cohorts is technically unfeasible due to time and accuracy concerns. Automated tools that can detect WMHs robustly and with high accuracy are needed. Here, we present and validate a fully automatic Technique for segmentation and volumetric quantification of WMHs in aging and AD. The proposed Technique combines intensity and location features frommultiplemagnetic resonance imaging contrasts and manually labeled training data with a linear classifier to perform fast and robust segmentations. It provides both a continuous subject specific WMH map reflecting different levels of tissue damage and binary segmentations. Themethodwas used to detectWMHs in 80 elderly/AD brains (ADC data set) as well as 40 healthy subjects at risk of AD (PREVENT-AD data set). Robustness across different scanners was validated using ten subjects from ADNI2/GO study. Voxel-wise and volumetric agreements were evaluated using Dice similarity index (SI) and intra-class correlation (ICC), yielding ${\mathrm{ ICC}}=0.96$ , ${\mathrm{ SI}}= 0.62\pm 0.16$ for ADC data set and ${\mathrm{ ICC}}=0.78$ , ${\mathrm{ SI}}=0.51\pm 0.15$ for PREVENT-AD data set. The proposed method was robust in the independent sample yielding ${\mathrm{ SI}}=0.64\pm 0.17$ with ${\mathrm{ ICC}}=0.93$ for ADNI2/GO subjects. The proposed method provides fast, accurate, and robust segmentations on previously unseen data from different models of scanners, making it ideal to study WMHs in large scale multi-site studies.
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validation of a Regression Technique for segmentation of white matter hyperintensities in alzheimer s disease
IEEE Transactions on Medical Imaging, 2017Co-Authors: Mahsa Dadar, Tharick A Pascoal, Sarinporn Manitsirikul, Karen Misquitta, Vladimir S Fonov, Carmela M Tartaglia, John C S Breitner, Pedro Rosaneto, Owen Carmichael, Charles DecarliAbstract:Segmentation and volumetric quantification of white matter hyperintensities (WMHs) is essential in assessment and monitoring of the vascular burden in aging and Alzheimer’s disease (AD), especially when considering their effect on cognition. Manually segmenting WMHs in large cohorts is technically unfeasible due to time and accuracy concerns. Automated tools that can detect WMHs robustly and with high accuracy are needed. Here, we present and validate a fully automatic Technique for segmentation and volumetric quantification of WMHs in aging and AD. The proposed Technique combines intensity and location features frommultiplemagnetic resonance imaging contrasts and manually labeled training data with a linear classifier to perform fast and robust segmentations. It provides both a continuous subject specific WMH map reflecting different levels of tissue damage and binary segmentations. Themethodwas used to detectWMHs in 80 elderly/AD brains (ADC data set) as well as 40 healthy subjects at risk of AD (PREVENT-AD data set). Robustness across different scanners was validated using ten subjects from ADNI2/GO study. Voxel-wise and volumetric agreements were evaluated using Dice similarity index (SI) and intra-class correlation (ICC), yielding ${\mathrm{ ICC}}=0.96$ , ${\mathrm{ SI}}= 0.62\pm 0.16$ for ADC data set and ${\mathrm{ ICC}}=0.78$ , ${\mathrm{ SI}}=0.51\pm 0.15$ for PREVENT-AD data set. The proposed method was robust in the independent sample yielding ${\mathrm{ SI}}=0.64\pm 0.17$ with ${\mathrm{ ICC}}=0.93$ for ADNI2/GO subjects. The proposed method provides fast, accurate, and robust segmentations on previously unseen data from different models of scanners, making it ideal to study WMHs in large scale multi-site studies.
Lorenzo Bruzzone - One of the best experts on this subject based on the ideXlab platform.
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estimating soil moisture with the support vector Regression Technique
IEEE Geoscience and Remote Sensing Letters, 2011Co-Authors: Luca Pasolli, Claudia Notarnicola, Lorenzo BruzzoneAbstract:This letter presents an experimental analysis of the application of the e-insensitive support vector Regression (SVR) Technique to soil moisture content estimation from remotely sensed data at field/basin scale. SVR has attractive properties, such as ease of use, good intrinsic generalization capability, and robustness to noise in the training data, which make it a valid candidate as an alternative to more traditional neural-network-based Techniques usually adopted in soil moisture content estimation. Its effectiveness in this application is assessed by using field measurements and considering various combinations of the input features (i.e., different active and/or passive microwave measurements acquired using various sensor frequencies, polarizations, and acquisition geometries). The performance of the SVR method (in terms of estimation accuracy, generalization capability, computational complexity, and ease of use) is compared with that achieved using a multilayer perceptron neural network, which is considered as a benchmark in the addressed application. This analysis provides useful indications for building soil moisture estimation processors for upcoming satellites or near-real-time applications.
Ataur Rahman - One of the best experts on this subject based on the ideXlab platform.
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water demand modelling using independent component Regression Technique
Water Resources Management, 2017Co-Authors: Md Mahmudul Haque, Amaury De Souza, Ataur RahmanAbstract:Water demand modelling is an active field of research. The modelling and forecasting tools are useful to get the estimation of forecasted water demand for different forecast horizons (e.g. 1 h to 10 years) in order to achieve more efficient and sustainable water resources management systems. However, modelling and forecasting of accurate water demand are challenging and difficult tasks. Several issues make the demand forecasting challenging such as the nature and quality of available data, numerous water demand variables, diversity in forecast horizons and geographical differences in modelling catchments. These issues have motivated a number of studies to be conducted to produce better water demand modelling and forecasting tools in order to improve forecast reliability. A variety of Techniques have been adopted in water demand forecasting, however, application of independent component Regression (ICR) Technique has not been investigated yet. Hence, this study explores, for the first time, the use of the ICR Technique in medium term urban water demand forecasting. This uses data from the city of Aquidauana, Brazil. It also compares the performance of the developed ICR model with two other commonly modeling methods, principal component Regression and multiple linear Regression models. It has been found that ICR model perform better than the other two models in modelling water demand with a higher performance indices (i.e. R 2 , RMSE, NSE and MARE) for the independent validation period. The results indicate that the ICR Technique has the potential to develop water demand models successfully. The methodology adopted in this paper can be applied to other countries to develop water demand forecasting model.
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regional flood frequency analysis in eastern australia bayesian gls Regression based methods within fixed region and roi framework quantile Regression vs parameter Regression Technique
Journal of Hydrology, 2012Co-Authors: Khaled Haddad, Ataur RahmanAbstract:Summary In this article, an approach using Bayesian Generalised Least Squares (BGLS) Regression in a region-of-influence (ROI) framework is proposed for regional flood frequency analysis (RFFA) for ungauged catchments. Using the data from 399 catchments in eastern Australia, the BGLS-ROI is constructed to regionalise the flood quantiles (Quantile Regression Technique (QRT)) and the first three moments of the log-Pearson type 3 (LP3) distribution (Parameter Regression Technique (PRT)). This scheme firstly develops a fixed region model to select the best set of predictor variables for use in the subsequent Regression analyses using an approach that minimises the model error variance while also satisfying a number of statistical selection criteria. The identified optimal Regression equation is then used in the ROI experiment where the ROI is chosen for a site in question as the region that minimises the predictive uncertainty. To evaluate the overall performances of the quantiles estimated by the QRT and PRT, a one-at-a-time cross-validation procedure is applied. Results of the proposed method indicate that both the QRT and PRT in a BGLS-ROI framework lead to more accurate and reliable estimates of flood quantiles and moments of the LP3 distribution when compared to a fixed region approach. Also the BGLS-ROI can deal reasonably well with the heterogeneity in Australian catchments as evidenced by the Regression diagnostics. Based on the evaluation statistics it was found that both BGLS-QRT and PRT-ROI perform similarly well, which suggests that the PRT is a viable alternative to QRT in RFFA. The RFFA methods developed in this paper is based on the database available in eastern Australia. It is expected that availability of a more comprehensive database (in terms of both quality and quantity) will further improve the predictive performance of both the fixed and ROI based RFFA methods presented in this study, which however needs to be investigated in future when such a database is available.
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design flood estimation in ungauged catchments a comparison between the probabilistic rational method and quantile Regression Technique for nsw
Australian journal of water resources, 2011Co-Authors: Ataur Rahman, Khaled Haddad, Mohona Zaman, George Kuczera, P E WeinmannAbstract:Design flood estimation for ungauged catchments is often required in hydrologic design. The most commonly adopted regional flood frequency analysis methods used for this purpose include the index flood method, Regression based Techniques and various forms of the rational method. This paper first examines the similarities and differences between the probabilistic rational method (PRM) (the currently recommended method for Victoria and eastern NSW in Australian Rainfall and Runoff) and the generalised least squares (GLS) based quantile Regression Technique (QRT). It then uses data from 107 catchments in NSW to compare the performance of these two methods. To make a valid comparison, the same predictor variables and data set have been used for both methods.
Pingfeng Pai - One of the best experts on this subject based on the ideXlab platform.
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potential assessment of the support vector Regression Technique in rainfall forecasting
Water Resources Management, 2007Co-Authors: Weichiang Hong, Pingfeng PaiAbstract:Forecasting and monitoring of rainfall values are increasingly important for decreasing economic loss caused by flash floods. Based on statistical learning theory, support vector Regression (SVR) has been used to deal with forecasting problems. Performing structural risk minimization rather than minimizing the training errors, SVR algorithms have better generalization ability than the conventional artificial neural networks. The objective of this investigation is to examine the feasibility and applicability of SVR in forecasting volumes of rainfall during typhoon seasons. In addition, Simulated Annealing (SA) algorithms are employed to choose parameters of the SVR model. Subsequently, rainfall values during typhoon periods in Taiwan's Wu–Tu watershed are used to demonstrate the forecasting performance of the proposed model. The simulation results show that the proposed SVRSA model is a promising alternative in forecasting amounts of rainfall during typhoon seasons.
