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Classification of tensors and fiber tracts using Mercer-kernels encoding soft probabilistic spatial and diffusion information

11 years 11 months ago
Classification of tensors and fiber tracts using Mercer-kernels encoding soft probabilistic spatial and diffusion information
In this paper, we present a kernel-based approach to the clustering of diffusion tensors and fiber tracts. We propose to use a Mercer kernel over the tensor space where both spatial and diffusion information are taken into account. This kernel highlights implicitly the connectivity along fiber tracts. Tensor segmentation is performed using kernel-PCA compounded with a landmark-Isomap embedding and k-means clustering. Based on a soft fiber representation, we extend the tensor kernel to deal with fiber tracts using the multi-instance kernel that reflects not only interactions between points along fiber tracts, but also the interactions between diffusion tensors. This unsupervised method is further extended by way of an atlas-based registration of diffusion-free images, followed by a classification of fibers based on nonlinear kernel Support Vector Machines (SVMs). Promising experimental results of tensor and fiber classification of the human skeletal muscle over a significant set of hea...
Radhouène Neji, Nikos Paragios, Gilles Fleu
Added 16 Aug 2010
Updated 16 Aug 2010
Type Conference
Year 2009
Where CVPR
Authors Radhouène Neji, Nikos Paragios, Gilles Fleury, Jean-Philippe Thiran, Georg Langs
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