The Experts below are selected from a list of 82527 Experts worldwide ranked by ideXlab platform
Anas Kharboutly - One of the best experts on this subject based on the ideXlab platform.
-
Advanced Sensor Noise analysis for CT-scanner identification from its 3D images
2015Co-Authors: Anas Kharboutly, William Puech, Gérard SubsolAbstract:Medical image processing fuses the image processing technologies in the medical disciplines. Particularly, computed tomography images provide a 3D vision of any part of the human body. These 3D images are generated by CT-Scanner devices. In this paper, we propose an advanced method of CT-Scanner identification from its 3D images. Basically, we analyze the Sensor Noise in order to identify the source CT-Scanner. For each CT-Scanner, we build three dimension identifiers regarding the three directional axes `X', `Y' and `Z'. The dimension identifier consists of a reference pattern Noise and a correlation map. To identify the source CT-Scanner from a tested slice, we compute the correlation between each dimension identifier of each device and this tested slice. The highest correlation value represents an indicator to the source CT-Scanner and the acquisition directional axis. To isolate the pure Noise, we use a wavelet based denoising algorithm. Experiments are applied on three different CT-Scanners. 10 3D images are selected from each CT-Scanner, each 3D image is composed of 512 slices. As a result, we are able to identify the acquisition CT-Scanner and the acquisition dimensional axis.
-
Improving Sensor Noise analysis for CT-Scanner identification
2015 23rd European Signal Processing Conference (EUSIPCO), 2015Co-Authors: Anas Kharboutly, William Puech, Gérard SubsolAbstract:CT-Scanner devices produce three-dimensional images of the internal structure of the body. In this paper, we propose a method that is based on the analysis of Sensor Noise to identify the CT-Scanner device. For each CT-scanner we built a reference pattern Noise and a correlation map from its slices. Finally, we can correlate any test slice with the reference pattern Noise of each device according to its correlation map. This correlation map gives a weighting for each pixel regarding its position in the reference pattern Noise. We used a wavelet-based Wiener filter and an edge detection method to extract the Noise from a slice. Experiments were applied on three CT-Scanners with 40 3D images, including 3600 slices, and we demonstrate that we are able to identify each CT-Scanner separately.
-
EUSIPCO - Improving Sensor Noise analysis for CT-Scanner identification
2015 23rd European Signal Processing Conference (EUSIPCO), 2015Co-Authors: Anas Kharboutly, William Puech, Gérard Subsol, Denis HoaAbstract:CT-Scanner devices produce three-dimensional images of the internal structure of the body. In this paper, we propose a method that is based on the analysis of Sensor Noise to identify the CT-Scanner device. For each CT-scanner we built a reference pattern Noise and a correlation map from its slices. Finally, we can correlate any test slice with the reference pattern Noise of each device according to its correlation map. This correlation map gives a weighting for each pixel regarding its position in the reference pattern Noise. We used a wavelet-based Wiener filter and an edge detection method to extract the Noise from a slice. Experiments were applied on three CT-Scanners with 40 3D images, including 3600 slices, and we demonstrate that we are able to identify each CT-Scanner separately.
-
CT-Scanner identification based on Sensor Noise analysis
2014Co-Authors: Anas Kharboutly, William Puech, Gérard Subsol, Denis HoaAbstract:Medical image processing is considered as an important topic in the domain of image processing. It is used to help the doctors to improve and speed up the diagnosis process. In particular, computed tomography scanners (CT-Scanner) are used to create cross-sectional medical 3D images of bones. In this paper, we propose a method for CT-Scanner identification based on the Sensor Noise analysis. We built the reference Noise pattern for each CT-Scanner from its 3D image, then we correlated the tested 3D images with each reference Noise pattern in order to identify the corresponding CT-Scanner. We used a wavelet-based Wiener filter approach to extract the Noise. Experimental results were applied on eight 3D images of 100 slices from different CT-Scanners, and we were able to identify each CT-Scanner separately.
-
EUVIP - CT-Scanner identification based on Sensor Noise analysis
2014 5th European Workshop on Visual Information Processing (EUVIP), 2014Co-Authors: Anas Kharboutly, William Puech, Gérard Subsol, Denis HoaAbstract:Medical image processing is considered as an important topic in the domain of image processing. It is used to help the doctors to improve and speed up the diagnosis process. In particular, computed tomography scanners (CT-Scanner) are used to create cross-sectional medical 3D images of bones. In this paper, we propose a method for CT-Scanner identification based on the Sensor Noise analysis. We built the reference Noise pattern for each CT-Scanner from its 3D image, then we correlated the tested 3D images with each reference Noise pattern in order to identify the corresponding CT-Scanner. We used a wavelet-based Wiener filter approach to extract the Noise. Experimental results were applied on eight 3D images of 100 slices from different CT-Scanners, and we were able to identify each CT-Scanner separately.
Gérard Subsol - One of the best experts on this subject based on the ideXlab platform.
-
Advanced Sensor Noise analysis for CT-scanner identification from its 3D images
2015Co-Authors: Anas Kharboutly, William Puech, Gérard SubsolAbstract:Medical image processing fuses the image processing technologies in the medical disciplines. Particularly, computed tomography images provide a 3D vision of any part of the human body. These 3D images are generated by CT-Scanner devices. In this paper, we propose an advanced method of CT-Scanner identification from its 3D images. Basically, we analyze the Sensor Noise in order to identify the source CT-Scanner. For each CT-Scanner, we build three dimension identifiers regarding the three directional axes `X', `Y' and `Z'. The dimension identifier consists of a reference pattern Noise and a correlation map. To identify the source CT-Scanner from a tested slice, we compute the correlation between each dimension identifier of each device and this tested slice. The highest correlation value represents an indicator to the source CT-Scanner and the acquisition directional axis. To isolate the pure Noise, we use a wavelet based denoising algorithm. Experiments are applied on three different CT-Scanners. 10 3D images are selected from each CT-Scanner, each 3D image is composed of 512 slices. As a result, we are able to identify the acquisition CT-Scanner and the acquisition dimensional axis.
-
Improving Sensor Noise analysis for CT-Scanner identification
2015 23rd European Signal Processing Conference (EUSIPCO), 2015Co-Authors: Anas Kharboutly, William Puech, Gérard SubsolAbstract:CT-Scanner devices produce three-dimensional images of the internal structure of the body. In this paper, we propose a method that is based on the analysis of Sensor Noise to identify the CT-Scanner device. For each CT-scanner we built a reference pattern Noise and a correlation map from its slices. Finally, we can correlate any test slice with the reference pattern Noise of each device according to its correlation map. This correlation map gives a weighting for each pixel regarding its position in the reference pattern Noise. We used a wavelet-based Wiener filter and an edge detection method to extract the Noise from a slice. Experiments were applied on three CT-Scanners with 40 3D images, including 3600 slices, and we demonstrate that we are able to identify each CT-Scanner separately.
-
EUSIPCO - Improving Sensor Noise analysis for CT-Scanner identification
2015 23rd European Signal Processing Conference (EUSIPCO), 2015Co-Authors: Anas Kharboutly, William Puech, Gérard Subsol, Denis HoaAbstract:CT-Scanner devices produce three-dimensional images of the internal structure of the body. In this paper, we propose a method that is based on the analysis of Sensor Noise to identify the CT-Scanner device. For each CT-scanner we built a reference pattern Noise and a correlation map from its slices. Finally, we can correlate any test slice with the reference pattern Noise of each device according to its correlation map. This correlation map gives a weighting for each pixel regarding its position in the reference pattern Noise. We used a wavelet-based Wiener filter and an edge detection method to extract the Noise from a slice. Experiments were applied on three CT-Scanners with 40 3D images, including 3600 slices, and we demonstrate that we are able to identify each CT-Scanner separately.
-
CT-Scanner identification based on Sensor Noise analysis
2014Co-Authors: Anas Kharboutly, William Puech, Gérard Subsol, Denis HoaAbstract:Medical image processing is considered as an important topic in the domain of image processing. It is used to help the doctors to improve and speed up the diagnosis process. In particular, computed tomography scanners (CT-Scanner) are used to create cross-sectional medical 3D images of bones. In this paper, we propose a method for CT-Scanner identification based on the Sensor Noise analysis. We built the reference Noise pattern for each CT-Scanner from its 3D image, then we correlated the tested 3D images with each reference Noise pattern in order to identify the corresponding CT-Scanner. We used a wavelet-based Wiener filter approach to extract the Noise. Experimental results were applied on eight 3D images of 100 slices from different CT-Scanners, and we were able to identify each CT-Scanner separately.
-
EUVIP - CT-Scanner identification based on Sensor Noise analysis
2014 5th European Workshop on Visual Information Processing (EUVIP), 2014Co-Authors: Anas Kharboutly, William Puech, Gérard Subsol, Denis HoaAbstract:Medical image processing is considered as an important topic in the domain of image processing. It is used to help the doctors to improve and speed up the diagnosis process. In particular, computed tomography scanners (CT-Scanner) are used to create cross-sectional medical 3D images of bones. In this paper, we propose a method for CT-Scanner identification based on the Sensor Noise analysis. We built the reference Noise pattern for each CT-Scanner from its 3D image, then we correlated the tested 3D images with each reference Noise pattern in order to identify the corresponding CT-Scanner. We used a wavelet-based Wiener filter approach to extract the Noise. Experimental results were applied on eight 3D images of 100 slices from different CT-Scanners, and we were able to identify each CT-Scanner separately.
Leon M. Tolbert - One of the best experts on this subject based on the ideXlab platform.
-
Modular Multilevel Converter (MMC) Modeling Considering Submodule Voltage Sensor Noise
IEEE Transactions on Power Electronics, 2021Co-Authors: Xingxuan Huang, James Palmer, Fred Wang, Leon M. TolbertAbstract:The modular multilevel converter (MMC) is a popular topology in medium- and high-voltage applications, and many efforts have been spent on MMC modeling. However, the impact of submodule voltage Sensor Noise (SVSN), which becomes more severe due to increasing switching speed of power semiconductors and compact submodule design, has not been considered in conventional models. In this letter, the SVSN is introduced by coupling capacitances between the Sensor and power stage in an MMC switching model. Furthermore, the SVSN impact is considered in an MMC average model based on derivation of the relationship between the SVSN and the duty cycle. The proposed MMC switching model and average model considering the SVSN are validated by comparing simulations with experimental results in an MMC prototype using 10-kV SiC MOSFETS.
-
Improving Voltage Sensor Noise Immunity in a High Voltage and High dv/dt Environment
2020 IEEE Applied Power Electronics Conference and Exposition (APEC), 2020Co-Authors: James Palmer, Xingxuan Huang, Li Zhang, William Giewont, Fei Fred Wang, Leon M. TolbertAbstract:This work focuses on improving voltage Sensor Noise immunity in a high voltage and high dv/dt environment. This is demonstrated in a 10 kV SiC MOSFET based Modular Multilevel Converter phase leg. The design is improved through several iterations while employing methodologies such as shielding, PCB layout techniques, improving the signal-to-Noise ratio, and reducing the bandwidth of the Sensor to reduce the Noise impact of the high dv/dt of the SiC device. The impact of each methodology on the design is stressed, and the final version of the Sensor shows significant improvement in Noise immunity while offering the best high voltage design available.
Denis Hoa - One of the best experts on this subject based on the ideXlab platform.
-
EUSIPCO - Improving Sensor Noise analysis for CT-Scanner identification
2015 23rd European Signal Processing Conference (EUSIPCO), 2015Co-Authors: Anas Kharboutly, William Puech, Gérard Subsol, Denis HoaAbstract:CT-Scanner devices produce three-dimensional images of the internal structure of the body. In this paper, we propose a method that is based on the analysis of Sensor Noise to identify the CT-Scanner device. For each CT-scanner we built a reference pattern Noise and a correlation map from its slices. Finally, we can correlate any test slice with the reference pattern Noise of each device according to its correlation map. This correlation map gives a weighting for each pixel regarding its position in the reference pattern Noise. We used a wavelet-based Wiener filter and an edge detection method to extract the Noise from a slice. Experiments were applied on three CT-Scanners with 40 3D images, including 3600 slices, and we demonstrate that we are able to identify each CT-Scanner separately.
-
CT-Scanner identification based on Sensor Noise analysis
2014Co-Authors: Anas Kharboutly, William Puech, Gérard Subsol, Denis HoaAbstract:Medical image processing is considered as an important topic in the domain of image processing. It is used to help the doctors to improve and speed up the diagnosis process. In particular, computed tomography scanners (CT-Scanner) are used to create cross-sectional medical 3D images of bones. In this paper, we propose a method for CT-Scanner identification based on the Sensor Noise analysis. We built the reference Noise pattern for each CT-Scanner from its 3D image, then we correlated the tested 3D images with each reference Noise pattern in order to identify the corresponding CT-Scanner. We used a wavelet-based Wiener filter approach to extract the Noise. Experimental results were applied on eight 3D images of 100 slices from different CT-Scanners, and we were able to identify each CT-Scanner separately.
-
EUVIP - CT-Scanner identification based on Sensor Noise analysis
2014 5th European Workshop on Visual Information Processing (EUVIP), 2014Co-Authors: Anas Kharboutly, William Puech, Gérard Subsol, Denis HoaAbstract:Medical image processing is considered as an important topic in the domain of image processing. It is used to help the doctors to improve and speed up the diagnosis process. In particular, computed tomography scanners (CT-Scanner) are used to create cross-sectional medical 3D images of bones. In this paper, we propose a method for CT-Scanner identification based on the Sensor Noise analysis. We built the reference Noise pattern for each CT-Scanner from its 3D image, then we correlated the tested 3D images with each reference Noise pattern in order to identify the corresponding CT-Scanner. We used a wavelet-based Wiener filter approach to extract the Noise. Experimental results were applied on eight 3D images of 100 slices from different CT-Scanners, and we were able to identify each CT-Scanner separately.
J.-j. Fuchs - One of the best experts on this subject based on the ideXlab platform.
-
Estimation of the number of signals in the presence of unknown correlated Sensor Noise
IEEE Transactions on Signal Processing, 1992Co-Authors: J.-j. FuchsAbstract:A method for estimating the number of transmitted signals in the presence of spatially correlated Sensor Noise is proposed. The procedure is developed under the assumption that the unknown Noise covariance matrix is a band matrix. In practice, it is quite robust with respect to this finite correlation length assumption. In Sensor array processing, this amounts to assuming that the Noise field is locally correlated spatially. Since spatial stationarity of the Noise is not necessary, it also applies, for instance, to white Noise with different power along the array. Simulations indicate that the asymptotic analysis holds for quite small sample sizes. >
-
ICASSP - Estimation of the number of signals in the presence of unknown correlated Sensor Noise
International Conference on Acoustics Speech and Signal Processing, 1Co-Authors: J.-j. FuchsAbstract:A method for estimating the number of transmitted signals in the presence of spatially correlated Sensor Noise is proposed. The procedure applies when the unknown Noise covariance matrix is a band matrix. This amounts to assuming that the Noise field is only locally correlated, a quite realistic assumption in many situations. Since spatial stationarity of the Noise is not necessary it also applies, for instance, to white Noise with varying power along the array. Some simulations indicate that the asymptotic analysis holds for quite small sample sizes, in situations where, to the author's knowledge, no existing procedure would work. >
