The Experts below are selected from a list of 184077 Experts worldwide ranked by ideXlab platform

Le Wang - One of the best experts on this subject based on the ideXlab platform.

  • Brain MR Image Segmentation with Spatial Constrained K-Mean Algorithm and Dual-Tree Complex Wavelet Transform
    Journal of medical systems, 2014
    Co-Authors: Jingdan Zhang, Wuhan Jiang, Ruichun Wang, Le Wang
    Abstract:

    In brain MR images, the noise and low-contrast significantly deteriorate the segmentation results. In this paper, we propose an automatic unsupervised segmentation method integrating dual-tree complex wavelet transform (DT-CWT) with K-Mean Algorithm for brain MR image. Firstly, a multi-dimensional feature vector is constructed based on the intensity, the low-frequency subband of DT-CWT and spatial position information. Then, a spatial constrained K-Mean Algorithm is presented as the segmentation system. The proposed method is validated by extensive experiments using both simulated and real T1-weighted MR images, and compared with the state-of-the-art Algorithms.

Sylvain Rama - One of the best experts on this subject based on the ideXlab platform.

  • Shift and Mean Algorithm for Functional Imaging with High Spatio-Temporal Resolution.
    Frontiers in cellular neuroscience, 2015
    Co-Authors: Sylvain Rama
    Abstract:

    Understanding neuronal physiology requires to record electrical activity in many small and remote compartments such as dendrites, axon or dendritic spines. To do so, electrophysiology has long been the tool of choice, as it allows recording very subtle and fast changes in electrical activity. However, electrophysiological measurements are mostly limited to large neuronal compartments such as the neuronal soma. To overcome these limitations, optical methods have been developed, allowing the monitoring of changes in fluorescence of fluorescent reporter dyes inserted into the neuron, with a spatial resolution theoretically only limited by the dye wavelength and optical devices. However, the temporal and spatial resolutive power of functional fluorescence imaging of live neurons is often limited by a necessary trade-off between image resolution, signal to noise ratio (SNR) and speed of acquisition. Here, I propose to use a Super-Resolution Shift and Mean (S&M) Algorithm previously used in image computing to improve the SNR, time sampling and spatial resolution of acquired fluorescent signals. I demonstrate the benefits of this methodology using two examples: voltage imaging of action potentials (APs) in soma and dendrites of CA3 pyramidal cells and calcium imaging in the dendritic shaft and spines of CA3 pyramidal cells. I show that this Algorithm allows the recording of a broad area at low speed in order to achieve a high SNR, and then pick the signal in any small compartment and resample it at high speed. This method allows preserving both the SNR and the temporal resolution of the signal, while acquiring the original images at high spatial resolution.

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

  • Brain MR Image Segmentation with Spatial Constrained K-Mean Algorithm and Dual-Tree Complex Wavelet Transform
    Journal of medical systems, 2014
    Co-Authors: Jingdan Zhang, Wuhan Jiang, Ruichun Wang, Le Wang
    Abstract:

    In brain MR images, the noise and low-contrast significantly deteriorate the segmentation results. In this paper, we propose an automatic unsupervised segmentation method integrating dual-tree complex wavelet transform (DT-CWT) with K-Mean Algorithm for brain MR image. Firstly, a multi-dimensional feature vector is constructed based on the intensity, the low-frequency subband of DT-CWT and spatial position information. Then, a spatial constrained K-Mean Algorithm is presented as the segmentation system. The proposed method is validated by extensive experiments using both simulated and real T1-weighted MR images, and compared with the state-of-the-art Algorithms.

Wuhan Jiang - One of the best experts on this subject based on the ideXlab platform.

  • Brain MR Image Segmentation with Spatial Constrained K-Mean Algorithm and Dual-Tree Complex Wavelet Transform
    Journal of medical systems, 2014
    Co-Authors: Jingdan Zhang, Wuhan Jiang, Ruichun Wang, Le Wang
    Abstract:

    In brain MR images, the noise and low-contrast significantly deteriorate the segmentation results. In this paper, we propose an automatic unsupervised segmentation method integrating dual-tree complex wavelet transform (DT-CWT) with K-Mean Algorithm for brain MR image. Firstly, a multi-dimensional feature vector is constructed based on the intensity, the low-frequency subband of DT-CWT and spatial position information. Then, a spatial constrained K-Mean Algorithm is presented as the segmentation system. The proposed method is validated by extensive experiments using both simulated and real T1-weighted MR images, and compared with the state-of-the-art Algorithms.

Ruichun Wang - One of the best experts on this subject based on the ideXlab platform.

  • Brain MR Image Segmentation with Spatial Constrained K-Mean Algorithm and Dual-Tree Complex Wavelet Transform
    Journal of medical systems, 2014
    Co-Authors: Jingdan Zhang, Wuhan Jiang, Ruichun Wang, Le Wang
    Abstract:

    In brain MR images, the noise and low-contrast significantly deteriorate the segmentation results. In this paper, we propose an automatic unsupervised segmentation method integrating dual-tree complex wavelet transform (DT-CWT) with K-Mean Algorithm for brain MR image. Firstly, a multi-dimensional feature vector is constructed based on the intensity, the low-frequency subband of DT-CWT and spatial position information. Then, a spatial constrained K-Mean Algorithm is presented as the segmentation system. The proposed method is validated by extensive experiments using both simulated and real T1-weighted MR images, and compared with the state-of-the-art Algorithms.