The Experts below are selected from a list of 48273 Experts worldwide ranked by ideXlab platform
Eric C Wong - One of the best experts on this subject based on the ideXlab platform.
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a hypercapnia based Normalization Method for improved spatial localization of human brain activation with fmri
NMR in Biomedicine, 1997Co-Authors: Peter A Bandettini, Eric C WongAbstract:An issue in blood oxygenation level dependent contrast-based functional MRI is the accurate interpretation of the activation-induced signal changes. Hemodynamic factors other than activation-induced changes in blood oxygenation are known to contribute to the signal change magnitudes and dynamics, and therefore need to be accounted for or removed. In this paper, a general Method for removal of effects other than activation-induced blood oxygenation changes from fMRI brain activation maps by the use of hypercapnic stress Normalization is introduced. First, the effects of resting blood volume distribution across voxels on activation-induced BOLD-based fMRI signal changes are shown to be significant. Second, the effects of hypercapnia and hypoxia on resting and activation-induced signal changes are demonstrated. These results suggest that global hemodynamic stresses may be useful for non-invasive mapping of blood volume. Third, the Normalization technique is demonstrated. © 1997 John Wiley & Sons, Ltd.
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a hypercapnia based Normalization Method for improved spatial localization of human brain activation with fmri
NMR in Biomedicine, 1997Co-Authors: Peter A Bandettini, Eric C WongAbstract:An issue in blood oxygenation level dependent contrast-based functional MRI is the accurate interpretation of the activation-induced signal changes. Hemodynamic factors other than activation-induced changes in blood oxygenation are known to contribute to the signal change magnitudes and dynamics, and therefore need to be accounted for or removed. In this paper, a general Method for removal of effects other than activation-induced blood oxygenation changes from fMRI brain activation maps by the use of hypercapnic stress Normalization is introduced. First, the effects of resting blood volume distribution across voxels on activation-induced BOLD-based fMRI signal changes are shown to be significant. Second, the effects of hypercapnia and hypoxia on resting and activation-induced signal changes are demonstrated. These results suggest that global hemodynamic stresses may be useful for non-invasive mapping of blood volume. Third, the Normalization technique is demonstrated.
Peter A Bandettini - One of the best experts on this subject based on the ideXlab platform.
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a hypercapnia based Normalization Method for improved spatial localization of human brain activation with fmri
NMR in Biomedicine, 1997Co-Authors: Peter A Bandettini, Eric C WongAbstract:An issue in blood oxygenation level dependent contrast-based functional MRI is the accurate interpretation of the activation-induced signal changes. Hemodynamic factors other than activation-induced changes in blood oxygenation are known to contribute to the signal change magnitudes and dynamics, and therefore need to be accounted for or removed. In this paper, a general Method for removal of effects other than activation-induced blood oxygenation changes from fMRI brain activation maps by the use of hypercapnic stress Normalization is introduced. First, the effects of resting blood volume distribution across voxels on activation-induced BOLD-based fMRI signal changes are shown to be significant. Second, the effects of hypercapnia and hypoxia on resting and activation-induced signal changes are demonstrated. These results suggest that global hemodynamic stresses may be useful for non-invasive mapping of blood volume. Third, the Normalization technique is demonstrated. © 1997 John Wiley & Sons, Ltd.
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a hypercapnia based Normalization Method for improved spatial localization of human brain activation with fmri
NMR in Biomedicine, 1997Co-Authors: Peter A Bandettini, Eric C WongAbstract:An issue in blood oxygenation level dependent contrast-based functional MRI is the accurate interpretation of the activation-induced signal changes. Hemodynamic factors other than activation-induced changes in blood oxygenation are known to contribute to the signal change magnitudes and dynamics, and therefore need to be accounted for or removed. In this paper, a general Method for removal of effects other than activation-induced blood oxygenation changes from fMRI brain activation maps by the use of hypercapnic stress Normalization is introduced. First, the effects of resting blood volume distribution across voxels on activation-induced BOLD-based fMRI signal changes are shown to be significant. Second, the effects of hypercapnia and hypoxia on resting and activation-induced signal changes are demonstrated. These results suggest that global hemodynamic stresses may be useful for non-invasive mapping of blood volume. Third, the Normalization technique is demonstrated.
Kinman Lam - One of the best experts on this subject based on the ideXlab platform.
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an efficient illumination Normalization Method for face recognition
Pattern Recognition Letters, 2006Co-Authors: Xudong Xie, Kinman LamAbstract:In this paper, an efficient representation Method insensitive to varying illumination is proposed for human face recognition. Theoretical analysis based on the human face model and the illumination model shows that the effects of varying lighting on a human face image can be modeled by a sequence of multiplicative and additive noises. Instead of computing these noises, which is very difficult for real applications, we aim to reduce or even remove their effect. In our Method, a local Normalization technique is applied to an image, which can effectively and efficiently eliminate the effect of uneven illuminations while keeping the local statistical properties of the processed image the same as in the corresponding image under normal lighting condition. After processing, the image under varying illumination will have similar pixel values to the corresponding image that is under normal lighting condition. Then, the processed images are used for face recognition. The proposed algorithm has been evaluated based on the Yale database, the AR database, the PIE database, the YaleB database and the combined database by using different face recognition Methods such as PCA, ICA and Gabor wavelets. Consistent and promising results were obtained, which show that our Method can effectively eliminate the effect of uneven illumination and greatly improve the recognition results.
Steen Knudsen - One of the best experts on this subject based on the ideXlab platform.
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a new non linear Normalization Method for reducing variability in dna microarray experiments
Genome Biology, 2002Co-Authors: Christopher T Workman, Lars Juhl Jensen, Hanne Ostergaard Jarmer, Randy M Berka, Laurent Gautier, Henrik Bjorn Nielser, Hanshenrik Saxild, Claus Nielsen, Soren Brunak, Steen KnudsenAbstract:Microarray data are subject to multiple sources of variation, of which biological sources are of interest whereas most others are only confounding. Recent work has identified systematic sources of variation that are intensity-dependent and non-linear in nature. Systematic sources of variation are not limited to the differing properties of the cyanine dyes Cy5 and Cy3 as observed in cDNA arrays, but are the general case for both oligonucleotide microarray (Affymetrix GeneChips) and cDNA microarray data. Current Normalization techniques are most often linear and therefore not capable of fully correcting for these effects. We present here a simple and robust non-linear Method for Normalization using array signal distribution analysis and cubic splines. These Methods compared favorably to Normalization using robust local-linear regression (lowess). The application of these Methods to oligonucleotide arrays reduced the relative error between replicates by 5-10% compared with a standard global Normalization Method. Application to cDNA arrays showed improvements over the standard Method and over Cy3-Cy5 Normalization based on dye-swap replication. In addition, a set of known differentially regulated genes was ranked higher by the t-test. In either cDNA or Affymetrix technology, signal-dependent bias was more than ten times greater than the observed print-tip or spatial effects. Intensity-dependent Normalization is important for both high-density oligonucleotide array and cDNA array data. Both the regression and spline-based Methods described here performed better than existing linear Methods when assessed on the variability of replicate arrays. Dye-swap Normalization was less effective at Cy3-Cy5 Normalization than either regression or spline-based Methods alone.
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a new non linear Normalization Method for reducing variability in dna microarray experiments
Genome Biology, 2002Co-Authors: Christopher T Workman, Lars Juhl Jensen, Hanne Ostergaard Jarmer, Randy M Berka, Laurent Gautier, Henrik Bjorn Nielser, Hanshenrik Saxild, Claus Nielsen, Soren Brunak, Steen KnudsenAbstract:Background Microarray data are subject to multiple sources of variation, of which biological sources are of interest whereas most others are only confounding. Recent work has identified systematic sources of variation that are intensity-dependent and non-linear in nature. Systematic sources of variation are not limited to the differing properties of the cyanine dyes Cy5 and Cy3 as observed in cDNA arrays, but are the general case for both oligonucleotide microarray (Affymetrix GeneChips) and cDNA microarray data. Current Normalization techniques are most often linear and therefore not capable of fully correcting for these effects.
Xinyu Zhu - One of the best experts on this subject based on the ideXlab platform.
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a mixed radiometric Normalization Method for mosaicking of high resolution satellite imagery
IEEE Transactions on Geoscience and Remote Sensing, 2017Co-Authors: Yongjun Zhang, Mingwei Sun, Xinyu ZhuAbstract:A new mixed radiometric Normalization (MRN) Method is introduced in this paper which aims to eliminate the radiometric difference in image mosaicking. The radiometric Normalization Methods can be classified as the absolute and relative approaches in traditional solutions. Though the absolute Methods could get the precise surface reflectance values of the images, rigorous conditions required for them are usually difficult to obtain, which makes the absolute Methods impractical in many cases. The relative Methods, which are simple and practicable, are more widely applied. However, the standard for designating the reference image needed for these Methods is not unified. Moreover, the color error propagation and the two-body problems are common obstacles for the relative Methods. The proposed MRN approach combines absolute and relative radiometric Normalization Methods, by which the advantages of both can be fully used and the limitations can be effectively avoided. First, suitable image after absolute radiometric calibration is selected as the reference image. Then, the invariant feature probability between the pixels of the target image and that of the reference image is obtained. Afterward, an adaptive local approach is adopted to obtain a suitable linear regression model for each block. Finally, a bilinear interpolation Method is employed to obtain the radiometric calibration parameters for each pixel. Moreover, the CIELAB color space is adopted to evaluate the results quantitatively. Experimental results of ZY-3, GF-1, and GF-2 data indicate that the proposed Method can eliminate the radiometric differences between images from the same or even different sensors.
