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In this paper, we describe a statistical video representation and modeling scheme. Video representation schemes are needed to segment a video stream into meaningful video-objects, useful for later indexing and retrieval applications. In the proposed methodology, unsupervised clustering via Gaussian mixture modeling extracts coherent space-time regions in(More)
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(More)
In this work we describe a novel statistical video representation and modeling scheme. Video representation schemes are needed to enable segmenting a video stream into meaningful video-objects, useful for later indexing and retrieval applications. In the proposed methodology , unsupervised clustering via Guassian mixture modeling extracts coherent(More)
In the last two decades functional magnetic resonance imaging (fMRI) has dominated research in neuroscience. However, only recently has it taken the first steps in translation to the clinical field. In this paper we describe the advantages of fMRI and DTI and the possible benefits of implementing these methods in clinical practice. We review the current(More)
In this paper, we present a robust approach to the registration of white matter tractographies extracted from diffusion tensor-magnetic resonance imaging scans. The fibers are projected into a high dimensional feature space based on the sequence of their 3-D coordinates. Adaptive mean-shift clustering is applied to extract a compact set of representative(More)
A supervised framework is presented for the automatic registration and segmentation of white matter (WM) tractographies extracted from brain DT-MRI. The framework relies on the direct registration between the fibers, without requiring any intensity-based registration as preprocessing. An affine transform is recovered together with a set of segmented fibers.(More)
In this paper we present a robust approach to the registration of white matter tractographies extracted from DT-MRI scans. The fibers are projected into a high dimensional feature space defined by the sequence of their 3D coordinates. Adaptive mean-shift (AMS) clustering is applied to extract a compact set of representative fiber-modes (FM). Each FM is(More)
We present a new method for direct inter and intra-subject registration of White Matter (WM) tractographies. The method does not require any previous registration between the brains, such as DTI registration. The algorithm is inspired by the well known iterative closest point method. Here, 3D points are replaced by feature vectors representing WM fibers,(More)
We present an efficient and robust method for direct registration between fiber bundles of interest and the complete White Matter (WM) tractography of the same or another brain. The method does not require any previous registration between the brains, such as DTI registration, and it can be used for both intra and inter-subject registration. The algorithm(More)