The Experts below are selected from a list of 180 Experts worldwide ranked by ideXlab platform
Hubert H. G. Savenije - One of the best experts on this subject based on the ideXlab platform.
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HESS Opinions: Linking Darcy's equation to the linear reservoir
Hydrology and Earth System Sciences, 2018Co-Authors: Hubert H. G. SavenijeAbstract:Abstract. In groundwater hydrology, two simple linear equations exist describing the relation between groundwater flow and the gradient driving it: Darcy's equation and the linear reservoir. Both equations are empirical and straightforward, but work at different scales: Darcy's equation at the laboratory scale and the linear reservoir at the watershed scale. Although at first sight they appear similar, it is not trivial to upscale Darcy's equation to the watershed scale without detailed knowledge of the structure or shape of the underlying aquifers. This paper shows that these two equations, combined by the water balance, are indeed identical provided there is equal resistance in space for water entering the subsurface network. This implies that groundwater systems make use of an efficient drainage network, a mostly invisible pattern that has evolved over geological timescales. This drainage network provides equally distributed resistance for water to access the system, connecting the active groundwater body to the stream, much like a leaf is organized to provide all stomata access to moisture at equal resistance. As a result, the timescale of the linear reservoir appears to be inversely proportional to Darcy's “conductance”, the proportionality being the product of the porosity and the resistance to entering the drainage network. The main question remaining is which physical law lies behind pattern formation in groundwater systems, evolving in a way that resistance to drainage is constant in space. But that is a fundamental question that is equally relevant for understanding the hydraulic properties of leaf Veins in plants or of Blood Veins in animals.
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Opinion paper: Linking Darcy's equation to the linear reservoir
2017Co-Authors: Hubert H. G. SavenijeAbstract:In groundwater hydrology, two simple linear equations exist describing the relation between groundwater flow and the gradient driving it: Darcy's equation and the linear reservoir. Both equations are empirical and straightforward, but work at different scales: Darcy's equation at the laboratory scale and the linear reservoir at the watershed scale. Although at first sight they appear similar, it is not trivial to upscale Darcy's equation to the watershed scale without detailed knowledge of the structure or shape of the underlying aquifers. This paper shows that these two equations, combined by the water balance, are indeed identical provided there is equal resistance in space for water entering the subsurface network. This implies that groundwater systems make use of an efficient drainage network, a mostly invisible pattern that has evolved over geological time scales. This drainage network provides equally distributed resistance for water to exit the system, connecting the active groundwater body to the stream, much like a leaf is organized to provide all stomata access to moisture at equal resistance. As a result, the residence time of the linear reservoir appears to be inversely proportional to Darcy's conductance , the proportionality being the product of the porosity and the resistance to entering the drainage network. The main question remaining is which physical law lies behind pattern formation in groundwater systems, evolving in a way that resistance to drainage is constant in space. But that is a physical question that is equally relevant for understanding the hydraulic properties of leaf Veins in plants or of Blood Veins in animals.
N. Karthikeyan - One of the best experts on this subject based on the ideXlab platform.
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Volumetric analysis framework for accurate segmentation and classification (VAF-ASC) of lung tumor from CT images
Soft Computing, 2020Co-Authors: M. S. Kavitha, J. Shanthini, N. KarthikeyanAbstract:Lung tumor can be typically stated as the abnormal cell growth in lungs that may cause severe threat to patient health, since lung is a significant organ which comprises associated network of Blood Veins and lymphatic canals. The earlier detection and classification of lung tumor creates a greater impact on increasing the survival rate of patients. For analysis, the Computed Tomography (CT) lung images are broadly used, since it gives information about the various lung regions. The prediction of tumor contour, position, and volume plays an imperative role in accurate segmentation and classification of tumor cells. This will aid in successful tumor stage detection and treatment phases. With that concern, this paper develops a Volumetric Analysis Framework for Accurate Segmentation and Classification of lung tumors. The volumetric analysis framework comprises the estimation of length, thickness, and height of the detected tumor cell for achieving précised results. Though there are many models for tumor detection from 2D CT inputs, it is very important to develop a method for lung nodule separation from noisy background. For that, this paper connectivity and locality features of the lung image pixels. Moreover, morphological processing techniques are incorporated for removing the additional noises and airways. Tumor segmentation has been accomplished by the k -means clustering approach. Tumor Nodule Metastasis classification based-volumetric analysis is performed for accurate results. The Volumetric Analysis Framework provides better results with respect to factors such as accuracy rate of tumor diagnosis, reduced computation time, and appropriate tumor stage classification.
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Volumetric analysis framework for accurate segmentation and classification (VAF-ASC) of lung tumor from CT images
Soft Computing, 2020Co-Authors: M. S. Kavitha, J. Shanthini, N. KarthikeyanAbstract:Lung tumor can be typically stated as the abnormal cell growth in lungs that may cause severe threat to patient health, since lung is a significant organ which comprises associated network of Blood Veins and lymphatic canals. The earlier detection and classification of lung tumor creates a greater impact on increasing the survival rate of patients. For analysis, the Computed Tomography (CT) lung images are broadly used, since it gives information about the various lung regions. The prediction of tumor contour, position, and volume plays an imperative role in accurate segmentation and classification of tumor cells. This will aid in successful tumor stage detection and treatment phases. With that concern, this paper develops a Volumetric Analysis Framework for Accurate Segmentation and Classification of lung tumors. The volumetric analysis framework comprises the estimation of length, thickness, and height of the detected tumor cell for achieving precised results. Though there are many models for tumor detection from 2D CT inputs, it is very important to develop a method for lung nodule separation from noisy background. For that, this paper connectivity and locality features of the lung image pixels. Moreover, morphological processing techniques are incorporated for removing the additional noises and airways. Tumor segmentation has been accomplished by the k-means clustering approach. Tumor Nodule Metastasis classification based-volumetric analysis is performed for accurate results. The Volumetric Analysis Framework provides better results with respect to factors such as accuracy rate of tumor diagnosis, reduced computation time, and appropriate tumor stage classification.
M. S. Kavitha - One of the best experts on this subject based on the ideXlab platform.
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Volumetric analysis framework for accurate segmentation and classification (VAF-ASC) of lung tumor from CT images
Soft Computing, 2020Co-Authors: M. S. Kavitha, J. Shanthini, N. KarthikeyanAbstract:Lung tumor can be typically stated as the abnormal cell growth in lungs that may cause severe threat to patient health, since lung is a significant organ which comprises associated network of Blood Veins and lymphatic canals. The earlier detection and classification of lung tumor creates a greater impact on increasing the survival rate of patients. For analysis, the Computed Tomography (CT) lung images are broadly used, since it gives information about the various lung regions. The prediction of tumor contour, position, and volume plays an imperative role in accurate segmentation and classification of tumor cells. This will aid in successful tumor stage detection and treatment phases. With that concern, this paper develops a Volumetric Analysis Framework for Accurate Segmentation and Classification of lung tumors. The volumetric analysis framework comprises the estimation of length, thickness, and height of the detected tumor cell for achieving précised results. Though there are many models for tumor detection from 2D CT inputs, it is very important to develop a method for lung nodule separation from noisy background. For that, this paper connectivity and locality features of the lung image pixels. Moreover, morphological processing techniques are incorporated for removing the additional noises and airways. Tumor segmentation has been accomplished by the k -means clustering approach. Tumor Nodule Metastasis classification based-volumetric analysis is performed for accurate results. The Volumetric Analysis Framework provides better results with respect to factors such as accuracy rate of tumor diagnosis, reduced computation time, and appropriate tumor stage classification.
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Volumetric analysis framework for accurate segmentation and classification (VAF-ASC) of lung tumor from CT images
Soft Computing, 2020Co-Authors: M. S. Kavitha, J. Shanthini, N. KarthikeyanAbstract:Lung tumor can be typically stated as the abnormal cell growth in lungs that may cause severe threat to patient health, since lung is a significant organ which comprises associated network of Blood Veins and lymphatic canals. The earlier detection and classification of lung tumor creates a greater impact on increasing the survival rate of patients. For analysis, the Computed Tomography (CT) lung images are broadly used, since it gives information about the various lung regions. The prediction of tumor contour, position, and volume plays an imperative role in accurate segmentation and classification of tumor cells. This will aid in successful tumor stage detection and treatment phases. With that concern, this paper develops a Volumetric Analysis Framework for Accurate Segmentation and Classification of lung tumors. The volumetric analysis framework comprises the estimation of length, thickness, and height of the detected tumor cell for achieving precised results. Though there are many models for tumor detection from 2D CT inputs, it is very important to develop a method for lung nodule separation from noisy background. For that, this paper connectivity and locality features of the lung image pixels. Moreover, morphological processing techniques are incorporated for removing the additional noises and airways. Tumor segmentation has been accomplished by the k-means clustering approach. Tumor Nodule Metastasis classification based-volumetric analysis is performed for accurate results. The Volumetric Analysis Framework provides better results with respect to factors such as accuracy rate of tumor diagnosis, reduced computation time, and appropriate tumor stage classification.
Veerle Darras - One of the best experts on this subject based on the ideXlab platform.
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Renal and hepatic distribution of type I and type III iodothyronine deiodinase protein in chicken
Journal of Endocrinology, 2004Co-Authors: Carla Verhoelst, S. Van Der Geyten, Veerle DarrasAbstract:Iodothyronine deiodinase in vitro activity studies in the chicken showed the presence of type I and type III iodothyronine deiodinase activity in both liver and kidney. Due to the lack of a specific antiserum the cellular localization of the deiodinase proteins could not be revealed until now. In the present study, specific antisera were used to study the renal and hepatic distribution of type I and type III iodothyronine deiodinase protein in the chicken. Immunocytochemical staining of liver tissue led to an immunopositive signal in the hepatocytes in general. Moreover, a zonal distribution could be detected for both enzymes. Maximum protein expression was shown in a thin layer of hepatocytes bordering the Blood Veins. Although pericentral localization of type I deiodinase protein has been previously reported in the rat, no data were given concerning type III deiodinase protein. In the present study, we report the co-localization of both enzymes in the chicken. Co-expression of the deiodinases was also found in the kidney. Expression of both proteins was associated with the tubular epithelial cells and with the transitional epithelium, and the inner longitudinal and outer circular muscle layers of the ureter. No staining could be detected in the lamina propria or in the fat tissue surrounding the ureter.
J. Shanthini - One of the best experts on this subject based on the ideXlab platform.
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Volumetric analysis framework for accurate segmentation and classification (VAF-ASC) of lung tumor from CT images
Soft Computing, 2020Co-Authors: M. S. Kavitha, J. Shanthini, N. KarthikeyanAbstract:Lung tumor can be typically stated as the abnormal cell growth in lungs that may cause severe threat to patient health, since lung is a significant organ which comprises associated network of Blood Veins and lymphatic canals. The earlier detection and classification of lung tumor creates a greater impact on increasing the survival rate of patients. For analysis, the Computed Tomography (CT) lung images are broadly used, since it gives information about the various lung regions. The prediction of tumor contour, position, and volume plays an imperative role in accurate segmentation and classification of tumor cells. This will aid in successful tumor stage detection and treatment phases. With that concern, this paper develops a Volumetric Analysis Framework for Accurate Segmentation and Classification of lung tumors. The volumetric analysis framework comprises the estimation of length, thickness, and height of the detected tumor cell for achieving précised results. Though there are many models for tumor detection from 2D CT inputs, it is very important to develop a method for lung nodule separation from noisy background. For that, this paper connectivity and locality features of the lung image pixels. Moreover, morphological processing techniques are incorporated for removing the additional noises and airways. Tumor segmentation has been accomplished by the k -means clustering approach. Tumor Nodule Metastasis classification based-volumetric analysis is performed for accurate results. The Volumetric Analysis Framework provides better results with respect to factors such as accuracy rate of tumor diagnosis, reduced computation time, and appropriate tumor stage classification.
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Volumetric analysis framework for accurate segmentation and classification (VAF-ASC) of lung tumor from CT images
Soft Computing, 2020Co-Authors: M. S. Kavitha, J. Shanthini, N. KarthikeyanAbstract:Lung tumor can be typically stated as the abnormal cell growth in lungs that may cause severe threat to patient health, since lung is a significant organ which comprises associated network of Blood Veins and lymphatic canals. The earlier detection and classification of lung tumor creates a greater impact on increasing the survival rate of patients. For analysis, the Computed Tomography (CT) lung images are broadly used, since it gives information about the various lung regions. The prediction of tumor contour, position, and volume plays an imperative role in accurate segmentation and classification of tumor cells. This will aid in successful tumor stage detection and treatment phases. With that concern, this paper develops a Volumetric Analysis Framework for Accurate Segmentation and Classification of lung tumors. The volumetric analysis framework comprises the estimation of length, thickness, and height of the detected tumor cell for achieving precised results. Though there are many models for tumor detection from 2D CT inputs, it is very important to develop a method for lung nodule separation from noisy background. For that, this paper connectivity and locality features of the lung image pixels. Moreover, morphological processing techniques are incorporated for removing the additional noises and airways. Tumor segmentation has been accomplished by the k-means clustering approach. Tumor Nodule Metastasis classification based-volumetric analysis is performed for accurate results. The Volumetric Analysis Framework provides better results with respect to factors such as accuracy rate of tumor diagnosis, reduced computation time, and appropriate tumor stage classification.