The Experts below are selected from a list of 2151 Experts worldwide ranked by ideXlab platform
Duo Zhang - One of the best experts on this subject based on the ideXlab platform.
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a lattice boltzmann study on the impact of the geometrical properties of porous media on the steady state relative permeabilities on two phase immiscible flows
Advances in Water Resources, 2016Co-Authors: Duo Zhang, K Papadikis, Sai GuAbstract:In the current paper, the effect of the geometrical characteristics of 2-D porous media on the relative permeability in immiscible two-phase flows is studied. The generation of the different artificial porous media is performed using a Boolean model based on a random distribution of overlapping circles/ellipses, the size and shape of which are chosen to satisfy the specific Minkowski Functionals (i.e. volume fraction, solid line contour length, connectivity). The study aims to identify how each different Minkowski Functional affects the relative permeability of each phase at various saturations of the non-wetting phase. A 2-D multi-relaxation time (MRT) lattice Boltzmann model (LBM) that can handle high density ratios is employed in the simulation. The relationship between the driving forces G and the relative permeabilities of the two phases for every artificial structure is quantified. It is found that for high non-wetting phase saturations (fully connected flow), a non-linear relationship exists between the non-wetting phase flow rate and the driving force, whilst this relationship becomes linear at higher magnitudes of the latter. The force magnitude required to approach the linear region is highly influenced by the pore size distribution and the connectivity of the solid phase. For lower non-wetting phase saturation values, its relative permeability in the linear regime decreases as the fraction of small pores in the structure increases and the non-wetting phase flow becomes disconnected. A strong influence of the solid phase connectivity is also observed.
Axel Wismüller - One of the best experts on this subject based on the ideXlab platform.
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Predicting the biomechanical strength of proximal femur specimens with Minkowski Functionals and support vector regression
Proceedings of SPIE--the International Society for Optical Engineering, 2014Co-Authors: Chien-chun Yang, Mahesh B. Nagarajan, Markus B. Huber, Julio Carballido-gamio, Jan S. Bauer, Thomas Baum, Felix Eckstein, Eva-maria Lochmüller, Thomas M. Link, Axel WismüllerAbstract:Regional trabecular bone quality estimation for purposes of femoral bone strength prediction is important for improving the clinical assessment of osteoporotic fracture risk. In this study, we explore the ability of 3D Minkowski Functionals derived from multi-detector computed tomography (MDCT) images of proximal femur specimens in predicting their corresponding biomechanical strength. MDCT scans were acquired for 50 proximal femur specimens harvested from human cadavers. An automated volume of interest (VOI)-fitting algorithm was used to define a consistent volume in the femoral head of each specimen. In these VOIs, the trabecular bone micro-architecture was characterized by statistical moments of its BMD distribution and by topological features derived from Minkowski Functionals. A linear multiregression analysis and a support vector regression (SVR) algorithm with a linear kernel were used to predict the failure load (FL) from the feature sets; the predicted FL was compared to the true FL determined through biomechanical testing. The prediction performance was measured by the root mean square error (RMSE) for each feature set. The best prediction result was obtained from the Minkowski Functional surface used in combination with SVR, which had the lowest prediction error (RMSE = 0.939 ± 0.345) and which was significantly lower than mean BMD (RMSE = 1.075 ± 0.279, p
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Classification of small lesions in dynamic breast MRI: eliminating the need for precise lesion segmentation through spatio-temporal analysis of contrast enhancement
Machine Vision and Applications, 2013Co-Authors: Mahesh B. Nagarajan, Markus B. Huber, Thomas Schlossbauer, Gerda Leinsinger, Andrzej Krol, Axel WismüllerAbstract:Characterizing the dignity of breast lesions as benign or malignant is specifically difficult for small lesions; they do not exhibit typical characteristics of malignancy and are harder to segment since margins are harder to visualize. Previous attempts at using dynamic or morphologic criteria to classify small lesions (mean lesion diameter of about 1 cm) have not yielded satisfactory results. The goal of this work was to improve the classification performance in such small diagnostically challenging lesions while concurrently eliminating the need for precise lesion segmentation. To this end, we introduce a method for topological characterization of lesion enhancement patterns over time. Three Minkowski Functionals were extracted from all five post-contrast images of 60 annotated lesions on dynamic breast MRI exams. For each Minkowski Functional, topological features extracted from each post-contrast image of the lesions were combined into a high-dimensional texture feature vector. These feature vectors were classified in a machine learning task with support vector regression. For comparison, conventional Haralick texture features derived from gray-level co-occurrence matrices (GLCM) were used. A new method for extracting thresholded GLCM features was also introduced and investigated here. The best classification performance was observed with Minkowski Functionals area and perimeter , thresholded GLCM features f8 and f9, and conventional GLCM features f4 and f6. However, both Minkowski Functionals and thresholded GLCM achieved such results without lesion segmentation while the performance of GLCM features significantly deteriorated when lesions were not segmented ( $$p
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classification of small lesions in dynamic breast mri eliminating the need for precise lesion segmentation through spatio temporal analysis of contrast enhancement
Machine Vision Applications, 2013Co-Authors: Mahesh B. Nagarajan, Markus B. Huber, Thomas Schlossbauer, Gerda Leinsinger, Andrzej Krol, Axel WismüllerAbstract:Characterizing the dignity of breast lesions as benign or malignant is specifically difficult for small lesions; they do not exhibit typical characteristics of malignancy and are harder to segment since margins are harder to visualize. Previous attempts at using dynamic or morphologic criteria to classify small lesions (mean lesion diameter of about 1 cm) have not yielded satisfactory results. The goal of this work was to improve the classification performance in such small diagnostically challenging lesions while concurrently eliminating the need for precise lesion segmentation. To this end, we introduce a method for topological characterization of lesion enhancement patterns over time. Three Minkowski Functionals were extracted from all five post-contrast images of 60 annotated lesions on dynamic breast MRI exams. For each Minkowski Functional, topological features extracted from each post-contrast image of the lesions were combined into a high-dimensional texture feature vector. These feature vectors were classified in a machine learning task with support vector regression. For comparison, conventional Haralick texture features derived from gray-level co-occurrence matrices (GLCM) were used. A new method for extracting thresholded GLCM features was also introduced and investigated here. The best classification performance was observed with Minkowski Functionals area and perimeter, thresholded GLCM features f8 and f9, and conventional GLCM features f4 and f6. However, both Minkowski Functionals and thresholded GLCM achieved such results without lesion segmentation while the performance of GLCM features significantly deteriorated when lesions were not segmented ( $$p<0.05$$ ). This suggests that such advanced spatio-temporal characterization can improve the classification performance achieved in such small lesions, while simultaneously eliminating the need for precise segmentation.
Matthias Ostermann - One of the best experts on this subject based on the ideXlab platform.
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direct Minkowski Functional analysis of large redshift surveys a new high speed code tested on the luminous red galaxy sloan digital sky survey dr7 catalogue
Monthly Notices of the Royal Astronomical Society, 2014Co-Authors: Alexander Wiegand, Thomas Buchert, Matthias OstermannAbstract:As deeper galaxy catalogues are soon to come, it becomes even more important to measure large‐scale fluctuations in the catalogues with robust statistics that cover all moments of the galaxy distribution. In this paper we reinforce a direct analysis of galaxy data by employing the Germ‐Grain method to calculate the family of Minkowski Functionals. We introduce a new code, suitable for the analysis of large data sets without smoothing and without the construction of excursion sets. We provide new tools to measure correlation properties, putting emphasis on explicitly isolating non‐Gaussian correlations with the help of integral‐ geometric relations. As a first application we present the analysis of large‐scale fluctuations in the LRG sample of SDSS DR7 data. We find significant (more than 2 sigma) deviations from the CDM mock catalogues on samples as large as 500h 1 Mpc and 700h 1 Mpc, respectively, and we investigate possible sources of these deviations.
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direct Minkowski Functional analysis of large redshift surveys a new high speed code tested on the lrg sdss dr7 catalogue
arXiv: Cosmology and Nongalactic Astrophysics, 2013Co-Authors: Alexander Wiegand, Thomas Buchert, Matthias OstermannAbstract:As deeper galaxy catalogues are soon to come, it becomes even more important to measure large-scale fluctuations in the catalogues with robust statistics that cover all moments of the galaxy distribution. In this paper we reinforce a direct analysis of galaxy data by employing the Germ-Grain method to calculate the family of Minkowski Functionals. We introduce a new code, suitable for the analysis of large data sets without smoothing and without the construction of excursion sets. We provide new tools to measure correlation properties, putting emphasis on explicitly isolating non-Gaussian correlations with the help of integral--geometric relations. As a first application we present the analysis of large-scale fluctuations in the LRG sample of SDSS DR7 data. We find significant (more than 2-sigma) deviations from the simulated mock catalogues on samples as large as $500h^{-1}$Mpc and $700h^{-1}$Mpc, respectively, and we investigate possible sources of these deviations.
Wen Zhao - One of the best experts on this subject based on the ideXlab platform.
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Probing the statistical properties of CMB B-mode polarization through Minkowski Functionals
Journal of Cosmology and Astroparticle Physics, 2016Co-Authors: Larissa Santos, Kai Wang, Wen ZhaoAbstract:The detection of the magnetic type $B$-mode polarization is the main goal of future cosmic microwave background (CMB) experiments. In the standard model, the $B$-mode map is a strongly non-gaussian field due to the lensed component. Besides the two-point correlation function, the other statistics are also very important to dig the information of the polarization map. In this paper, we employ the Minkowski Functionals to study the morphological properties of the lensed $B$-mode maps. We find that the deviations from Gaussianity are very significant for both full and partial-sky surveys. As an application of the analysis, we investigate the morphological imprints of the foreground residuals in the $B$-mode map. We find that even for very tiny foreground residuals, the effects on the map can be detected by the Minkowski Functional analysis. Therefore, it provides a complementary way to investigate the foreground contaminations in the CMB studies.
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Probing the CMB cold spot through local Minkowski Functionals
Research in Astronomy and Astrophysics, 2014Co-Authors: Wen ZhaoAbstract:Both the Wilkinson Microwave Anisotropy Probe (WMAP) and Planck missions have reported an extremely cold spot (CS) centered at Galactic coordinate (l = 209 ° , b = -57 ° ) in the cosmic microwave background map. We study the local non-Gaussianity of the CS by defining local Minkowski Functionals. We find that the third Minkowski Functional ν 2 is quite sensitive to the non-Gaussianity caused by the CS. Compared with random Gaussian simulations, the WMAP CS deviates from Gaussianity at more than a 99% confidence level with a scale of R ~ 10 ° . Meanwhile, we find that cosmic texture provides an excellent explanation for these anomalies related to the WMAP CS, which could be further tested by future polarization data.
Alexander Wiegand - One of the best experts on this subject based on the ideXlab platform.
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direct Minkowski Functional analysis of large redshift surveys a new high speed code tested on the luminous red galaxy sloan digital sky survey dr7 catalogue
Monthly Notices of the Royal Astronomical Society, 2014Co-Authors: Alexander Wiegand, Thomas Buchert, Matthias OstermannAbstract:As deeper galaxy catalogues are soon to come, it becomes even more important to measure large‐scale fluctuations in the catalogues with robust statistics that cover all moments of the galaxy distribution. In this paper we reinforce a direct analysis of galaxy data by employing the Germ‐Grain method to calculate the family of Minkowski Functionals. We introduce a new code, suitable for the analysis of large data sets without smoothing and without the construction of excursion sets. We provide new tools to measure correlation properties, putting emphasis on explicitly isolating non‐Gaussian correlations with the help of integral‐ geometric relations. As a first application we present the analysis of large‐scale fluctuations in the LRG sample of SDSS DR7 data. We find significant (more than 2 sigma) deviations from the CDM mock catalogues on samples as large as 500h 1 Mpc and 700h 1 Mpc, respectively, and we investigate possible sources of these deviations.
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direct Minkowski Functional analysis of large redshift surveys a new high speed code tested on the lrg sdss dr7 catalogue
arXiv: Cosmology and Nongalactic Astrophysics, 2013Co-Authors: Alexander Wiegand, Thomas Buchert, Matthias OstermannAbstract:As deeper galaxy catalogues are soon to come, it becomes even more important to measure large-scale fluctuations in the catalogues with robust statistics that cover all moments of the galaxy distribution. In this paper we reinforce a direct analysis of galaxy data by employing the Germ-Grain method to calculate the family of Minkowski Functionals. We introduce a new code, suitable for the analysis of large data sets without smoothing and without the construction of excursion sets. We provide new tools to measure correlation properties, putting emphasis on explicitly isolating non-Gaussian correlations with the help of integral--geometric relations. As a first application we present the analysis of large-scale fluctuations in the LRG sample of SDSS DR7 data. We find significant (more than 2-sigma) deviations from the simulated mock catalogues on samples as large as $500h^{-1}$Mpc and $700h^{-1}$Mpc, respectively, and we investigate possible sources of these deviations.