The Experts below are selected from a list of 216 Experts worldwide ranked by ideXlab platform
Jamuna Kanta Sing - One of the best experts on this subject based on the ideXlab platform.
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local contextual information and Gaussian function induced fuzzy clustering algorithm for brain mr image segmentation and intensity inhomogeneity estimation
Applied Soft Computing, 2018Co-Authors: Nabanita Mahata, Sayan Kahali, Sudip Kumar Adhikari, Jamuna Kanta SingAbstract:Abstract This paper presents a fuzzy clustering algorithm, where local contextual information and a Gaussian function are incorporated into the objective function, for simultaneous brain MR image segmentation and intensity inhomogeneity estimation. In doing so, for each pixel, we define a local contextual information, which actually defines its association among the other neighboring pixels based on intensity distribution. In particular, this information defines the possibility of the pixel to belong into a specific tissue type. Whereas, for each tissue region, a Gaussian Surface is defined to estimate the intensity inhomogeneity (IIH) using the local image gradients, which are believed to be caused by the IIH. We use this Gaussian Surface to compensate the effect of IIH. In addition, for each pixel, we have introduced global and local membership functions, which in combined in association with the other parameters are responsible for generation of cluster prototypes. The IIH of the entire image region is iteratively removed from the image and the final segmentation result is obtained based on the global membership values. The simulation results on two benchmarks brain MR image databases and four volumes of real-patient brain MR image data show its efficiency and superiority over other fuzzy-based clustering algorithms.
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convolution of 3d Gaussian Surfaces for volumetric intensity inhomogeneity estimation and correction in 3d brain mr image data
Iet Computer Vision, 2018Co-Authors: Sayan Kahali, Sudip Kumar Adhikari, Jamuna Kanta SingAbstract:Magnetic resonance (MR) imaging technique has become indispensable in image-guided diagnosis and clinical research. However, present MR image acquisition leads to a slow varying intensity inhomogeneity (IIH) in MR image data. This study presents a novel technique based on convolution of three-dimensional (3D) Gaussian Surfaces, which is denoted as ‘Co3DGS’, for volumetric IIH estimation and correction for 3D brain MR image data. A 3D Gaussian Surface is approximated using local voxel gradients on each tissue volume corresponding to grey matter, white matter and cerebrospinal fluid of the 3D brain MR image data and then convolved to partially estimate the IIH, which is subsequently removed from the image data. The above processes are repeated until there is no such significant change in the voxel gradients. The Co3DGS technique has been tested on both synthetic and in-vivo human 3D brain MR image data of different pulse sequences. The empirical results both in qualitatively and quantitatively, which include coefficient of joint variation, index of variation, index of joint variation, index of class separability and root mean square error, collectively demonstrate that the Co3DGS efficiently estimates and removes the IIH from the 3D brain MR image data and stands superior to some state-of-the-art methods.
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on estimation of bias field in mri images polynomial vs Gaussian Surface fitting method
Journal of Chemometrics, 2016Co-Authors: Sayan Kahali, Sudip Kumar Adhikari, Jamuna Kanta SingAbstract:Surface fitting is one of the well-known retrospective methods for bias field estimation from magnetic resonance imaging (MRI) images. Bias field in MRI images is primarily caused because of radio frequency–coil nonuniformity, improper image acquisition process, patient movement, and so on. The bias field can be characterized by any slow variant and smooth function because of its slow variant nature. In this paper, we present a comparative study between polynomial and Gaussian Surface fitting methods. In particular, we have used both the second- and third-order polynomial functions to estimate the bias field. In this study, we approximate the bias field in two different ways. In the first method, the Surfaces are fitted on the anatomical tissue regions individually and then fused to estimate the bias field. Conversely, in the second method, we have done the same over the entire image region. We have tested on three volumes of simulated and one volume of real-patient MRI brain images and validated the results by both the qualitative and quantitative analyses. The quantitative analyses are presented in standard deviation and coefficient of joint variation. The analysis of the simulation results show that the Gaussian Surface fitting method yields better results in both the cases, where the Surface fitting is done on entire image and individual tissue regions.
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a nonparametric method for intensity inhomogeneity correction in mri brain images by fusion of Gaussian Surfaces
Signal Image and Video Processing, 2015Co-Authors: Sudip Kumar Adhikari, Jamuna Kanta Sing, Dipak Kumar Basu, Mita Nasipuri, Punam K. SahaAbstract:Intensity inhomogeneity (IIH) or bias field in magnetic resonance imaging (MRI) severely affects quantitative image analysis. This paper presents a nonparametric IIH-correction strategy in MRI brain images by fusing multiple Gaussian Surfaces. The IIH is modeled as a slowly varying multiplicative noise along with the actual tissue signals. The method does not require a priori knowledge on the intensity probability distribution; rather, it works directly on spatial domains using local image gradients. The method has four steps. Firstly, it extracts different potential tissue regions by considering image histogram. Secondly, an approximated bias field is estimated by fitting a Gaussian Surface on the gradient map of each of the homogeneous tissue regions by considering its center as the center of mass. The intensity inhomogeneity field of the entire image is then obtained by fusion of these bias fields. Finally, this IIH field is iteratively removed from the image to obtain the IIH-corrected image. The proposed method is evaluated extensively on popular BrainWeb simulated MRI brain databases and also on some real-patient MRI brain images. Both qualitative and quantitative evaluations of the proposed method reveal its efficiency in removing the bias field in MRI brain images. The standard deviation, coefficient of variation of different tissue regions and coefficient of joint variation between gray matter and white matter are significantly reduced in greater proportion as compared to other standard methods in the case of T2-weighted MRI and come very closer to the ground truths.
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segmentation of mri brain images by incorporating intensity inhomogeneity and spatial information using probabilistic fuzzy c means clustering algorithm
International Conference on Communications, 2012Co-Authors: Sudip Kumar Adhikari, Jamuna Kanta Sing, Dipak Kumar Basu, Mita Nasipuri, Punam K. SahaAbstract:Segmentation of magnetic resonance imaging (MRI) brain images is an important task to analyze tissue structures of a human brain. Due to improper image acquisition systems, MRI images are generally corrupted by intensity inhomogeneity (IIH) or intensity nonuniformity (INU). Conventional methods try to segment MRI images using only spatial information about the distribution of pixel intensities and are highly sensitive to noise and the IIH or INU. This paper presents a method to segment MRI brain images by considering the INU and spatial information using fuzzy C-means (FCM) clustering algorithm. Firstly, the INU of MRI brain image is corrected using fusion of Gaussian Surfaces. The individual Gaussian Surface is estimated independently over the different homogeneous regions by considering its center as the center of mass of the respective homogeneous region. Secondly, the IIH corrected image is segmented using probabilistic FCM algorithm, which considers spatial features of image pixels. The experiments using 3D synthetic phantoms and real-patient MRI brain images reveal that the proposed method performs satisfactorily.
Emil Alexov - One of the best experts on this subject based on the ideXlab platform.
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on the dielectric constant of proteins smooth dielectric function for macromolecular modeling and its implementation in delphi
Journal of Chemical Theory and Computation, 2013Co-Authors: Lin Li, Chuan Li, Zhe Zhang, Emil AlexovAbstract:Implicit methods for modeling protein electrostatics require dielectric properties of the system to be known, in particular, the value of the dielectric constant of protein. While numerous values of the internal protein dielectric constant were reported in the literature, still there is no consensus of what the optimal value is. Perhaps this is due to the fact that the protein dielectric constant is not a “constant” but is a complex function reflecting the properties of the protein’s structure and sequence. Here, we report an implementation of a Gaussian-based approach to deliver the dielectric constant distribution throughout the protein and surrounding water phase by utilizing the 3D structure of the corresponding macromolecule. In contrast to previous reports, we construct a smooth dielectric function throughout the space of the system to be modeled rather than just constructing a “Gaussian Surface” or smoothing molecule–water boundary. Analysis on a large set of proteins shows that (a) the average die...
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on the dielectric constant of proteins smooth dielectric function for macromolecular modeling and its implementation in delphi
Journal of Chemical Theory and Computation, 2013Co-Authors: Zhe Zhang, Emil AlexovAbstract:Implicit methods for modeling protein electrostatics require dielectric properties of the system to be known, in particular, the value of the dielectric constant of protein. While numerous values of the internal protein dielectric constant were reported in the literature, still there is no consensus of what the optimal value is. Perhaps this is due to the fact that the protein dielectric constant is not a "constant" but is a complex function reflecting the properties of the protein's structure and sequence. Here, we report an implementation of a Gaussian-based approach to deliver the dielectric constant distribution throughout the protein and surrounding water phase by utilizing the 3D structure of the corresponding macromolecule. In contrast to previous reports, we construct a smooth dielectric function throughout the space of the system to be modeled rather than just constructing a "Gaussian Surface" or smoothing molecule-water boundary. Analysis on a large set of proteins shows that (a) the average dielectric constant inside the protein is relatively low, about 6-7, and reaches a value of about 20-30 at the protein's Surface, and (b) high average local dielectric constant values are associated with charged residues while low dielectric constant values are automatically assigned to the regions occupied by hydrophobic residues. In terms of energetics, a benchmarking test was carried out against the experimental pKa's of 89 residues in staphylococcal nuclease (SNase) and showed that it results in a much better RMSD (= 1.77 pK) than the corresponding calculations done with a homogeneous high dielectric constant with an optimal value of 10 (RMSD = 2.43 pK).
Sudip Kumar Adhikari - One of the best experts on this subject based on the ideXlab platform.
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local contextual information and Gaussian function induced fuzzy clustering algorithm for brain mr image segmentation and intensity inhomogeneity estimation
Applied Soft Computing, 2018Co-Authors: Nabanita Mahata, Sayan Kahali, Sudip Kumar Adhikari, Jamuna Kanta SingAbstract:Abstract This paper presents a fuzzy clustering algorithm, where local contextual information and a Gaussian function are incorporated into the objective function, for simultaneous brain MR image segmentation and intensity inhomogeneity estimation. In doing so, for each pixel, we define a local contextual information, which actually defines its association among the other neighboring pixels based on intensity distribution. In particular, this information defines the possibility of the pixel to belong into a specific tissue type. Whereas, for each tissue region, a Gaussian Surface is defined to estimate the intensity inhomogeneity (IIH) using the local image gradients, which are believed to be caused by the IIH. We use this Gaussian Surface to compensate the effect of IIH. In addition, for each pixel, we have introduced global and local membership functions, which in combined in association with the other parameters are responsible for generation of cluster prototypes. The IIH of the entire image region is iteratively removed from the image and the final segmentation result is obtained based on the global membership values. The simulation results on two benchmarks brain MR image databases and four volumes of real-patient brain MR image data show its efficiency and superiority over other fuzzy-based clustering algorithms.
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convolution of 3d Gaussian Surfaces for volumetric intensity inhomogeneity estimation and correction in 3d brain mr image data
Iet Computer Vision, 2018Co-Authors: Sayan Kahali, Sudip Kumar Adhikari, Jamuna Kanta SingAbstract:Magnetic resonance (MR) imaging technique has become indispensable in image-guided diagnosis and clinical research. However, present MR image acquisition leads to a slow varying intensity inhomogeneity (IIH) in MR image data. This study presents a novel technique based on convolution of three-dimensional (3D) Gaussian Surfaces, which is denoted as ‘Co3DGS’, for volumetric IIH estimation and correction for 3D brain MR image data. A 3D Gaussian Surface is approximated using local voxel gradients on each tissue volume corresponding to grey matter, white matter and cerebrospinal fluid of the 3D brain MR image data and then convolved to partially estimate the IIH, which is subsequently removed from the image data. The above processes are repeated until there is no such significant change in the voxel gradients. The Co3DGS technique has been tested on both synthetic and in-vivo human 3D brain MR image data of different pulse sequences. The empirical results both in qualitatively and quantitatively, which include coefficient of joint variation, index of variation, index of joint variation, index of class separability and root mean square error, collectively demonstrate that the Co3DGS efficiently estimates and removes the IIH from the 3D brain MR image data and stands superior to some state-of-the-art methods.
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on estimation of bias field in mri images polynomial vs Gaussian Surface fitting method
Journal of Chemometrics, 2016Co-Authors: Sayan Kahali, Sudip Kumar Adhikari, Jamuna Kanta SingAbstract:Surface fitting is one of the well-known retrospective methods for bias field estimation from magnetic resonance imaging (MRI) images. Bias field in MRI images is primarily caused because of radio frequency–coil nonuniformity, improper image acquisition process, patient movement, and so on. The bias field can be characterized by any slow variant and smooth function because of its slow variant nature. In this paper, we present a comparative study between polynomial and Gaussian Surface fitting methods. In particular, we have used both the second- and third-order polynomial functions to estimate the bias field. In this study, we approximate the bias field in two different ways. In the first method, the Surfaces are fitted on the anatomical tissue regions individually and then fused to estimate the bias field. Conversely, in the second method, we have done the same over the entire image region. We have tested on three volumes of simulated and one volume of real-patient MRI brain images and validated the results by both the qualitative and quantitative analyses. The quantitative analyses are presented in standard deviation and coefficient of joint variation. The analysis of the simulation results show that the Gaussian Surface fitting method yields better results in both the cases, where the Surface fitting is done on entire image and individual tissue regions.
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a nonparametric method for intensity inhomogeneity correction in mri brain images by fusion of Gaussian Surfaces
Signal Image and Video Processing, 2015Co-Authors: Sudip Kumar Adhikari, Jamuna Kanta Sing, Dipak Kumar Basu, Mita Nasipuri, Punam K. SahaAbstract:Intensity inhomogeneity (IIH) or bias field in magnetic resonance imaging (MRI) severely affects quantitative image analysis. This paper presents a nonparametric IIH-correction strategy in MRI brain images by fusing multiple Gaussian Surfaces. The IIH is modeled as a slowly varying multiplicative noise along with the actual tissue signals. The method does not require a priori knowledge on the intensity probability distribution; rather, it works directly on spatial domains using local image gradients. The method has four steps. Firstly, it extracts different potential tissue regions by considering image histogram. Secondly, an approximated bias field is estimated by fitting a Gaussian Surface on the gradient map of each of the homogeneous tissue regions by considering its center as the center of mass. The intensity inhomogeneity field of the entire image is then obtained by fusion of these bias fields. Finally, this IIH field is iteratively removed from the image to obtain the IIH-corrected image. The proposed method is evaluated extensively on popular BrainWeb simulated MRI brain databases and also on some real-patient MRI brain images. Both qualitative and quantitative evaluations of the proposed method reveal its efficiency in removing the bias field in MRI brain images. The standard deviation, coefficient of variation of different tissue regions and coefficient of joint variation between gray matter and white matter are significantly reduced in greater proportion as compared to other standard methods in the case of T2-weighted MRI and come very closer to the ground truths.
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segmentation of mri brain images by incorporating intensity inhomogeneity and spatial information using probabilistic fuzzy c means clustering algorithm
International Conference on Communications, 2012Co-Authors: Sudip Kumar Adhikari, Jamuna Kanta Sing, Dipak Kumar Basu, Mita Nasipuri, Punam K. SahaAbstract:Segmentation of magnetic resonance imaging (MRI) brain images is an important task to analyze tissue structures of a human brain. Due to improper image acquisition systems, MRI images are generally corrupted by intensity inhomogeneity (IIH) or intensity nonuniformity (INU). Conventional methods try to segment MRI images using only spatial information about the distribution of pixel intensities and are highly sensitive to noise and the IIH or INU. This paper presents a method to segment MRI brain images by considering the INU and spatial information using fuzzy C-means (FCM) clustering algorithm. Firstly, the INU of MRI brain image is corrected using fusion of Gaussian Surfaces. The individual Gaussian Surface is estimated independently over the different homogeneous regions by considering its center as the center of mass of the respective homogeneous region. Secondly, the IIH corrected image is segmented using probabilistic FCM algorithm, which considers spatial features of image pixels. The experiments using 3D synthetic phantoms and real-patient MRI brain images reveal that the proposed method performs satisfactorily.
Yuming Wang - One of the best experts on this subject based on the ideXlab platform.
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Evolution of bi-Gaussian Surface parameters and sealing performance for a gas face seal under a low-speed condition
Tribology International, 2018Co-Authors: Weifeng Huang, Xi Shi, Zhike Peng, Xiangfeng Liu, Yuming WangAbstract:Abstract A spiral groove gas face seal, serving as a typical non-contacting seal, commonly encounters face contact during a low-speed condition such as the startup, shutdown, barring and warming-up operations. By using a discrete manner, the Surface-topography evolution of the two mated sealing rings during a low-speed barring process is achieved, and is in a great agreement with the previous standard tests. The bi-Gaussian Surface-topography evolution is found to well identify the end running-in, material adhesion and deep scratches. Furthermore, the Surface-topography evolution exhibits a high correlation with the recorded seal-performance indexes including leakage rate, torque and acoustic emission signal, which is helpful in exploring the deterioration mechanism of sealing performance.
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bi Gaussian Surface identification and reconstruction with revised autocorrelation functions
Tribology International, 2017Co-Authors: Noël Brunetière, Weifeng Huang, Xiangfeng Liu, Yuming WangAbstract:Abstract The newly proposed continuous separation method for bi-Gaussian stratified Surfaces, as the improvement of the existing ISO segmented one, is investigated. The continuous method provides an accurate Surface-separation solution but is much more efficient. It has great stability to resist the fluctuations of the probability material ratio curves that are universally observed on real engineering Surfaces. In contrast to the ISO segmented method, the continuous one is roughness-scale independent. When using a bi-Gaussian approach, it is difficult to identify component correlation lengths because of missing points in each individual component. An iterative process is used to overcome the defect in determining autocorrelation function (ACF). The ACF quality of the revised bi-Gaussian approach is better than that of the Johnson approach.
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Evolution of bi-Gaussian Surface parameters of silicon-carbide and carbon-graphite discs in a dry sliding wear process
Tribology International, 2017Co-Authors: Noël Brunetière, Weifeng Huang, Xiangfeng Liu, Yuming WangAbstract:Abstract A Surface-Surface dry sliding wear of silicon-carbide and carbon-graphite discs is performed on a rotational tribological tester. Coefficient of friction (COF) is monitored as a function of time. Samples are measured by a white light interferometer. Four uniform groups, each with two repetitions, are set to different test durations to investigate the evolution of bi-Gaussian Surface parameters in a wear process. The results show that the hard sample has a smoothed upper component with time, but a slightly changed lower component due to wear-generated debris. The wear-generated debris, however, introduce deep scratches to the soft sample before its upper component being altered, yielding a sharp lower component. Furthermore, the correlation between bi-Gaussian Surface parameters and COF is explored.
Xiangfeng Liu - One of the best experts on this subject based on the ideXlab platform.
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mechanism of bi Gaussian Surface topographies on generating acoustic emissions under a sliding friction
Tribology International, 2019Co-Authors: Weifeng Huang, Xi Shi, Zhike Peng, Xiangfeng LiuAbstract:Abstract Researchers revealed the acoustic-emission (AE) mechanism under a sliding friction by establishing an AE root-mean-square (RMS) model where the summit contact was demonstrated as the main AE source. However, only the summit on the upper component of a bi-Gaussian stratified Surface can confront a contact and become an AE source. A deterministic AE RMS model is established and applied to real and simulated Surfaces to reveal the mechanism of bi-Gaussian stratified topographies on generating AE RMSs. The AE RMS is strongly dependent on component Surface parameters rather than entire ones. The Surface-height RMS value and the proportion of the upper component are, respectively, positively and negatively related to the AE RMS value by affecting the summit density and curvature radius.
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Evolution of bi-Gaussian Surface parameters and sealing performance for a gas face seal under a low-speed condition
Tribology International, 2018Co-Authors: Weifeng Huang, Xi Shi, Zhike Peng, Xiangfeng Liu, Yuming WangAbstract:Abstract A spiral groove gas face seal, serving as a typical non-contacting seal, commonly encounters face contact during a low-speed condition such as the startup, shutdown, barring and warming-up operations. By using a discrete manner, the Surface-topography evolution of the two mated sealing rings during a low-speed barring process is achieved, and is in a great agreement with the previous standard tests. The bi-Gaussian Surface-topography evolution is found to well identify the end running-in, material adhesion and deep scratches. Furthermore, the Surface-topography evolution exhibits a high correlation with the recorded seal-performance indexes including leakage rate, torque and acoustic emission signal, which is helpful in exploring the deterioration mechanism of sealing performance.
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bi Gaussian Surface identification and reconstruction with revised autocorrelation functions
Tribology International, 2017Co-Authors: Noël Brunetière, Weifeng Huang, Xiangfeng Liu, Yuming WangAbstract:Abstract The newly proposed continuous separation method for bi-Gaussian stratified Surfaces, as the improvement of the existing ISO segmented one, is investigated. The continuous method provides an accurate Surface-separation solution but is much more efficient. It has great stability to resist the fluctuations of the probability material ratio curves that are universally observed on real engineering Surfaces. In contrast to the ISO segmented method, the continuous one is roughness-scale independent. When using a bi-Gaussian approach, it is difficult to identify component correlation lengths because of missing points in each individual component. An iterative process is used to overcome the defect in determining autocorrelation function (ACF). The ACF quality of the revised bi-Gaussian approach is better than that of the Johnson approach.
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Evolution of bi-Gaussian Surface parameters of silicon-carbide and carbon-graphite discs in a dry sliding wear process
Tribology International, 2017Co-Authors: Noël Brunetière, Weifeng Huang, Xiangfeng Liu, Yuming WangAbstract:Abstract A Surface-Surface dry sliding wear of silicon-carbide and carbon-graphite discs is performed on a rotational tribological tester. Coefficient of friction (COF) is monitored as a function of time. Samples are measured by a white light interferometer. Four uniform groups, each with two repetitions, are set to different test durations to investigate the evolution of bi-Gaussian Surface parameters in a wear process. The results show that the hard sample has a smoothed upper component with time, but a slightly changed lower component due to wear-generated debris. The wear-generated debris, however, introduce deep scratches to the soft sample before its upper component being altered, yielding a sharp lower component. Furthermore, the correlation between bi-Gaussian Surface parameters and COF is explored.
