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Magnetic Resonance Imaging (MRI) has been widely used in medical diagnose because of its non-invasive manner and excellent depiction of soft-tissue changes. Recently, the compressive sensing (CS) theory has been applied to reconstruct the MR image from highly down-sampled k-space data, which can reduce the scanning duration. To obtain useful information as much as possible with the same sampling rate,...
Diffusion-weighted imaging and tractography can get information related to the macroscopic structure in vivo. High angular resolution diffusion imaging (HARDI), which offers a wide range of sampling data, has proven to better characterize complex intra-voxel structures compared to its predecessor diffusion tensor imaging (DTI). On the basis of HARDI, the data-driven approaches, such as spherical deconvolution...
This functional magnetic resonance imaging (fMRI) study aimed to distinguish neural activation associated with competition and collaboration using multivoxel pattern analysis (MVPA). For each participant, a searchlight-based MVPA was applied to select informative voxels within training data. The support vector machine with a radial basis function kernel was used to obtain classification accuracy of...
The imperfections in the radio-frequency coils or problems associated with the acquisition sequences may cause MRI intensity inhomogeneities, which may mislead image segmentation. Comparing the tradition fuzzy C means model, this paper adds the bias field information in the objective function for simultaneous correction of the bias field and accurately segmentation. In adaptive model, the bias field...
This paper describes results for an ongoing research on the segmentation of the posterior fossa and the structures it contains in fetal brain MR images. A semi-automatic segmentation algorithm based on Dijkstra's algorithm and a fast marching level set method is suggested. The algorithm has been tested on a small number of images and compared to manual segmentations done by experts. It shows reasonable...
This paper introduces a novel pixel-level image fusion framework based on structural similarity (SSIM). SSIM is an image quality assessment metric developed recently through comparing local patterns of pixel intensities from luminance, contrast and structure. In our scheme, the relationship of input images is classified three kinds of cases by contrasting the SSIM value of the original images with...
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