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In this study, the white matter, gray matter and the tissues affected by Multiple Sclerosis are segmented semi-automatically from Magnetic Resonance images using programming environments ITK (Insight Registration and Segmentation Toolkit), VTK (Visualization Toolkit) and MeVisLab (Medical Image Processing and Visualization).
An automated scheme for magnetic resonance imaging (MRI) brain segmentation is proposed. An adaptive mean-shift methodology is utilized in order to classify brain voxels into one of three main tissue types: gray matter, white matter, and cerebro-spinal fluid. The MRI image space is represented by a high-dimensional feature space that includes multimodal intensity features as well as spatial features...
In this paper, we present a new Markovian scheme for MRI segmentation using a priori knowledge obtained from probability maps. Indeed we propose to use both triplet Markov chain and a brain atlas containing prior expectations about the spatial localization of the different tissue classes, to segment the brain in gray matter, white matter and cerebro-spinal fluid in an unsupervised way. Experimental...
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...
We present a method for tissue classification based on diffusion-weighted imaging (DWI)/diffusion tensor imaging (DTI) data. Our motivation is that independent tissue segmentation based on DWI/DTI images provides complementary information to the tissue segmentation result using structural MRI data alone. The basis idea is to classify the brain into two compartments by utilizing the tissue contrast...
This paper presents a kernel-based method of correspondence detection in diffusion tensor images (DTI), a key step towards their deformable registration. The proposed method is driven by a few focus points chosen in white matter, characterized by a unique morphological signature which incorporates the anisotropy, orientation and the anatomic context by using "oriented" Gabor filters and...
We propose a novel fluid image registration strategy based on an information-theoretic measure, the Jensen-Renyi divergence (JRD) of two images. We modified the definition of JRD, which is based on the joint histogram of two images, to develop a variational approach in which driving forces are applied throughout the deforming image to maximize the JRD between it and the target image. A viscous fluid...
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