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The paper presents a novel method of multi-dimension brain tissue segmentation. Firstly, the ventricle extraction and strip-skull are adopted by level set method and regions merging with tags. Secondly, the noise of the spinal fluid is eliminated through improved level set method. Finally, the gray matter and white matter are extracted through covered background method and the brain tissue is segmented...
A validation framework for MR image segmentation is proposed in this paper. It includes three stages: intensity inhomogeneity (IIH) correction, noise suppression without blurring structures and tissue classification. Based on MR brain images, in the first stage, an improved process is used to implement IIH correction. Subsequently, a new enhancement method on moments for noise removal and edge sharpening...
A novel method for segmentation of brain tissues in MRI (magnetic resonance imaging) images is proposed in this paper. First, we reduce noise using a versatile wavelet-based filter. Subsequently, watershed algorithm is applied to brain tissues as an initial segmenting method. Normally, the result of classical watershed algorithm on grey-scale textured images such as tissue images is over-segmentation...
Diffusion tensor imaging (DTI) measures, such as fractional anisotropy (FA), and trace are very sensitive to noise contained in the acquired diffusion weighted images. Typical isotropic smoothing methods reduce the high spatial frequency image content and blur the image features. We hypothesized that the diffusion tensor would be an approximate anisotropic Gaussian filter function because the blur...
This paper presents a method to segment brain tissue from T1-weighted magnetic resonance (MR) images. A modified BayesShrink method is utilized to filter the image in wavelet transform domain before segmentation, where the shrinkage strength is automatically adjusted with respect to noise level. Then the fuzzy c-means clustering is applied to segment brain tissue into cerebrospinal fluid, gray matter...
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