The Experts below are selected from a list of 166599 Experts worldwide ranked by ideXlab platform

Jing Bai - One of the best experts on this subject based on the ideXlab platform.

  • Principal component analysis of dynamic fluorescence tomography in Measurement Space
    Physics in medicine and biology, 2012
    Co-Authors: Xin Liu, Bin Zhang, Jianwen Luo, Jing Bai
    Abstract:

    Challenges remain in resolving metabolic processes of drugs within small animals using a fluorescence tomographic image. In our previous work, using principal component analysis (PCA), we detected functional structures with different kinetic behaviors, where PCA was applied in fluorescence tomographic sequence (i.e. in the image Space). As a result, all Measurement data had to be reconstructed before performing PCA, which imposed a large computational burden. In this paper, we propose a new approach and apply PCA directly to fluorescence projection sequence (i.e. in the Measurement Space). Utilizing the compression property of PCA, it is possible to resolve regions with different kinetics by reconstructing only a few principal components. Hence, the computational cost can be significantly reduced. To evaluate the performance of the new method, numerical simulation and a phantom experiment are performed on a hybrid fluorescence and x-ray computed tomography imaging system. The results demonstrate that the proposed method greatly reduces the computational time compared with the previous method, while keeping a similar resolving capability.

Xin Liu - One of the best experts on this subject based on the ideXlab platform.

  • Performance evaluation of principal component analysis for dynamic fluorescence tomographic imaging in Measurement Space
    Optical Engineering, 2015
    Co-Authors: Xin Liu, Zhuangzhi Yan
    Abstract:

    Challenges remain in resolving drug (fluorescent biomarkers) distributions within small animals by fluorescence diffuse optical tomography (FDOT). Principal component analysis (PCA) provides the capability of detecting organs (functional structures) from dynamic FDOT images. However, the resolving performance of PCA may be affected by various experimental factors, e.g., the noise levels in Measurement data, the variance in optical properties, the number of acquired frames, and so on. To address the problem, based on a simulation model, we analyze and compare the performance of PCA when applied to three typical sets of experimental conditions (frames number, noise level, and optical properties). The results show that the noise is a critical factor affecting the performance of PCA. When input data containing a low noise (

  • Principal component analysis of dynamic fluorescence tomography in Measurement Space
    Physics in medicine and biology, 2012
    Co-Authors: Xin Liu, Bin Zhang, Jianwen Luo, Jing Bai
    Abstract:

    Challenges remain in resolving metabolic processes of drugs within small animals using a fluorescence tomographic image. In our previous work, using principal component analysis (PCA), we detected functional structures with different kinetic behaviors, where PCA was applied in fluorescence tomographic sequence (i.e. in the image Space). As a result, all Measurement data had to be reconstructed before performing PCA, which imposed a large computational burden. In this paper, we propose a new approach and apply PCA directly to fluorescence projection sequence (i.e. in the Measurement Space). Utilizing the compression property of PCA, it is possible to resolve regions with different kinetics by reconstructing only a few principal components. Hence, the computational cost can be significantly reduced. To evaluate the performance of the new method, numerical simulation and a phantom experiment are performed on a hybrid fluorescence and x-ray computed tomography imaging system. The results demonstrate that the proposed method greatly reduces the computational time compared with the previous method, while keeping a similar resolving capability.

Bin Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Principal component analysis of dynamic fluorescence tomography in Measurement Space
    Physics in medicine and biology, 2012
    Co-Authors: Xin Liu, Bin Zhang, Jianwen Luo, Jing Bai
    Abstract:

    Challenges remain in resolving metabolic processes of drugs within small animals using a fluorescence tomographic image. In our previous work, using principal component analysis (PCA), we detected functional structures with different kinetic behaviors, where PCA was applied in fluorescence tomographic sequence (i.e. in the image Space). As a result, all Measurement data had to be reconstructed before performing PCA, which imposed a large computational burden. In this paper, we propose a new approach and apply PCA directly to fluorescence projection sequence (i.e. in the Measurement Space). Utilizing the compression property of PCA, it is possible to resolve regions with different kinetics by reconstructing only a few principal components. Hence, the computational cost can be significantly reduced. To evaluate the performance of the new method, numerical simulation and a phantom experiment are performed on a hybrid fluorescence and x-ray computed tomography imaging system. The results demonstrate that the proposed method greatly reduces the computational time compared with the previous method, while keeping a similar resolving capability.

Jianwen Luo - One of the best experts on this subject based on the ideXlab platform.

  • Principal component analysis of dynamic fluorescence tomography in Measurement Space
    Physics in medicine and biology, 2012
    Co-Authors: Xin Liu, Bin Zhang, Jianwen Luo, Jing Bai
    Abstract:

    Challenges remain in resolving metabolic processes of drugs within small animals using a fluorescence tomographic image. In our previous work, using principal component analysis (PCA), we detected functional structures with different kinetic behaviors, where PCA was applied in fluorescence tomographic sequence (i.e. in the image Space). As a result, all Measurement data had to be reconstructed before performing PCA, which imposed a large computational burden. In this paper, we propose a new approach and apply PCA directly to fluorescence projection sequence (i.e. in the Measurement Space). Utilizing the compression property of PCA, it is possible to resolve regions with different kinetics by reconstructing only a few principal components. Hence, the computational cost can be significantly reduced. To evaluate the performance of the new method, numerical simulation and a phantom experiment are performed on a hybrid fluorescence and x-ray computed tomography imaging system. The results demonstrate that the proposed method greatly reduces the computational time compared with the previous method, while keeping a similar resolving capability.

Claude Delpha - One of the best experts on this subject based on the ideXlab platform.

  • On Improved Spread Spectrum Watermark Detection Under Compressive Sampling
    2014
    Co-Authors: Anirban Bose, Santi Prasad Maity, Claude Delpha
    Abstract:

    This paper studies performance of correlation based energy detection (ED) and generalized likelihood ratio test (GLRT) to resolve binary hypothesis on spread spectrum (SS) watermark in digital images under compressive sampling (CS) paradigm. Watermark information in the form of independent and identically distributed (i.i.d) Gaussian pattern is embedded during image acquisition at low Measurement Space i.e. CS platform. Diversity technique used in communication receiver is then applied to improve watermark detector performance. Simulation results highlight that at low (watermark) signal-to-noise ratio (WNR/SNR), GLRT based detector offers high probability of detection (PD) while ED performs almost at par with GLRT at high SNR i.e. at high Measurement Space for a given watermark power. Simulation results also show the improved detector performance compared to the conventional correlator-detector and existing CS based watermarking methods.

  • EUVIP - On improved spread spectrum watermark detection under compressive sampling
    2014 5th European Workshop on Visual Information Processing (EUVIP), 2014
    Co-Authors: Anirban Bose, Santi Prasad Maity, Claude Delpha
    Abstract:

    This paper studies performance of correlation based energy detection (ED) and generalized likelihood ratio test (GLRT) to resolve binary hypothesis on spread spectrum (SS) watermark in digital images under compressive sampling (CS) paradigm. Watermark information in the form of independent and identically distributed (i.i.d) Gaussian pattern is embedded during image acquisition at low Measurement Space i.e. CS platform. Diversity technique used in communication receiver is then applied to improve watermark detector performance. Simulation results highlight that at low (watermark) signal-to-noise ratio (WNR/SNR), GLRT based detector offers high probability of detection (PD) while ED performs almost at par with GLRT at high SNR i.e. at high Measurement Space for a given watermark power. Simulation results also show the improved detector performance compared to the conventional correlator-detector and existing CS based watermarking methods.