The Experts below are selected from a list of 8019 Experts worldwide ranked by ideXlab platform
Junichi Hori - One of the best experts on this subject based on the ideXlab platform.
-
spatio temporal cortical source imaging of brain electrical activity by means of time varying parametric Projection Filter
IEEE Transactions on Biomedical Engineering, 2004Co-Authors: Junichi Hori, M AibaAbstract:In the present study, we explore suitable spatio-temporal Filters for inverse estimation of an equivalent dipole-layer distribution from the scalp electroencephalogram (EEG) for imaging of brain electric sources. We propose a time-varying parametric Projection Filter (tPPF) for the spatio-temporal EEG analysis. The performance of this tPPF algorithm was evaluated by computer simulation studies. An inhomogeneous three-concentric-spheres model was used in the present simulation study to represent the head volume conductor. An equivalent dipole layer was used to represent equivalently brain electric sources and estimated from the scalp potentials. The tPPF Filter was tested to remove time-varying noise such as instantaneous artifacts caused by eyes-blink. The present simulation results indicate that the proposed time-variant tPPF method provides enhanced performance in rejecting time-varying noise, as compared with the time-invariant parametric Projection Filter.
-
equivalent dipole source imaging of brain electric activity by means of parametric Projection Filter
Annals of Biomedical Engineering, 2001Co-Authors: Junichi HoriAbstract:In the present study, spatial Filters for inverse estimation of an equivalent dipole layer from the scalp-recorded potentials have been explored for their suitability in achieving high-resolution electroencephalogram (EEG) imaging. The performance of the parametric Projection Filter (PPF), which we propose to use for high-resolution EEG imaging, has been evaluated by computer simulations in the presence of a priori information on noise. An inhomogeneous three-concentric-sphere head model was used in the present simulation study to represent the head volume conductor. An equivalent dipole layer was used to model brain electric sources and estimated from the scalp potentials. Various noise conditions were simulated and the parametric Projection Filter was compared with standard regularization procedures such as the truncated singular value decomposition (TSVD) and the Tikhonov regularization (TKNV). The present simulation results suggest that the proposed method performs better than that of commonly used inverse regularization techniques, such as the general inverse using the TSVD and the TKNV, when the correlation between the original source distribution and the noise distribution is low, and performs similarly when the correlation is high. A method for determining the optimum regularization parameter, which can be applied to parametric inverse techniques, has also been developed. © 2001 Biomedical Engineering Society. PAC01: 8757Nk, 0230Zz, 8719Nn, 0260Dc
M Aiba - One of the best experts on this subject based on the ideXlab platform.
-
spatio temporal cortical source imaging of brain electrical activity by means of time varying parametric Projection Filter
IEEE Transactions on Biomedical Engineering, 2004Co-Authors: Junichi Hori, M AibaAbstract:In the present study, we explore suitable spatio-temporal Filters for inverse estimation of an equivalent dipole-layer distribution from the scalp electroencephalogram (EEG) for imaging of brain electric sources. We propose a time-varying parametric Projection Filter (tPPF) for the spatio-temporal EEG analysis. The performance of this tPPF algorithm was evaluated by computer simulation studies. An inhomogeneous three-concentric-spheres model was used in the present simulation study to represent the head volume conductor. An equivalent dipole layer was used to represent equivalently brain electric sources and estimated from the scalp potentials. The tPPF Filter was tested to remove time-varying noise such as instantaneous artifacts caused by eyes-blink. The present simulation results indicate that the proposed time-variant tPPF method provides enhanced performance in rejecting time-varying noise, as compared with the time-invariant parametric Projection Filter.
Zhong Chen - One of the best experts on this subject based on the ideXlab platform.
-
multi contrast brain mri image super resolution with gradient guided edge enhancement
IEEE Access, 2018Co-Authors: Hong Zheng, Kun Zeng, Di Guo, Jiaxi Ying, Yu Yang, Xi Peng, Feng Huang, Zhong ChenAbstract:In magnetic resonance imaging (MRI), the super-resolution technology has played a great role in improving image quality. The aim of this paper is to improve edges of brain MRI by incorporating the gradient information of another contrast high-resolution image. Multi-contrast images are assumed to possess the same gradient direction in a local pattern. We proposed to establish a relation model of gradient value between different contrast images to restore a high-resolution image from its input low-resolution version. The similarity of image patches is employed to estimate intensity parameters, leading a more accurate reconstructed image. Then, an iterative back-Projection Filter is applied to the reconstructed image to further increase the image quality. The new approach is verified on synthetic and real brain MRI images and achieves higher visual quality and higher objective quality criteria than the compared state-of-the-art super-resolution approaches. The gradient information of the multi-contrast MRI images is very useful. With a proper relation model, the proposed method enhances image edges in MRI image super-resolution. Improving the MRI image resolution from very low-resolution observations is challenging. We tackle this problem by first modeling the relation of gradient value in multi-contrast MRI and then performing fast supper-resolution methods. This relation model may be helpful for other MRI reconstruction problems.
Hidemitsu Ogawa - One of the best experts on this subject based on the ideXlab platform.
-
image restoration by averaged Projection Filter
Systems and Computers in Japan, 1992Co-Authors: Yukihiko Yamashita, Hidemitsu OgawaAbstract:A restoration Filter called the averaged Projection Filter (APF) is proposed for the linear degradation model. The proposed Filter selects the image with the least deviation of noise component from among those minimizing the image component of the restored image in the averaged sense with respect to a set of original images. With the plain Projection Filter, the frequency of occurrence of images is ignored. In the averaged Projection Filter, the restoration quality of images occurring more frequently is improved at the expense of those occurring less frequently. A general form of the Filter is obtained by using functional analysis
-
properties of averaged Projection Filter for image restoration
Systems and Computers in Japan, 1992Co-Authors: Yukihiko Yamashita, Hidemitsu OgawaAbstract:The averaged Projection Filter (APF) is an optimal restoration Filter proposed by the present authors in another paper. Here, properties of the APF are discussed in detail. For example, the image component of the restored image with the APF coincides with the original image if the original image belongs to a subspace. For the original images outside the subspace, the image component becomes the oblique Projection onto a subspace of the original image in the mean square sense. A noise suppression mechanism of the APF also is clarified by using the null space of the APF. A simpler expression of the APF is given under some assumption. Finally, relations between the APF and the Wiener Filter are discussed.
Hong Zheng - One of the best experts on this subject based on the ideXlab platform.
-
multi contrast brain mri image super resolution with gradient guided edge enhancement
IEEE Access, 2018Co-Authors: Hong Zheng, Kun Zeng, Di Guo, Jiaxi Ying, Yu Yang, Xi Peng, Feng Huang, Zhong ChenAbstract:In magnetic resonance imaging (MRI), the super-resolution technology has played a great role in improving image quality. The aim of this paper is to improve edges of brain MRI by incorporating the gradient information of another contrast high-resolution image. Multi-contrast images are assumed to possess the same gradient direction in a local pattern. We proposed to establish a relation model of gradient value between different contrast images to restore a high-resolution image from its input low-resolution version. The similarity of image patches is employed to estimate intensity parameters, leading a more accurate reconstructed image. Then, an iterative back-Projection Filter is applied to the reconstructed image to further increase the image quality. The new approach is verified on synthetic and real brain MRI images and achieves higher visual quality and higher objective quality criteria than the compared state-of-the-art super-resolution approaches. The gradient information of the multi-contrast MRI images is very useful. With a proper relation model, the proposed method enhances image edges in MRI image super-resolution. Improving the MRI image resolution from very low-resolution observations is challenging. We tackle this problem by first modeling the relation of gradient value in multi-contrast MRI and then performing fast supper-resolution methods. This relation model may be helpful for other MRI reconstruction problems.
