Etienne von Lavante

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We propose a spectral learning approach to shape segmentation. The method is composed of a constrained spectral clustering algorithm that is used to supervise the segmentation of a shape from a training data set, followed by a probabilistic label transfer algorithm that is used to match two shapes and to transfer cluster labels from a training-shape to a(More)
In order to address the tendency of ultrasound B-Mode images to show a too small sizing of tumour masses in breast cancer diagnosis, a novel segmentation method has been introduced. In this paper it has been explored if this problem can be solved by incorporating strain parameters from ultra-sound elastography into a segmentation framework. By incorporating(More)
In this paper a new completely unsupervised mesh segmen-tation algorithm is proposed, which is based on the PCA interpretation of the Laplacian eigenvectors of the mesh and on parametric clustering using Gaussian mixtures. We analyse the geometric properties of these vectors and we devise a practical method that combines single-vector analysis with(More)
The assessment and diagnosis of breast cancer with ultrasound is a challenging problem due to the low contrast between cancer masses and benign tissue. Due to this low contrast it has proven to be difficult to achieve reliable segmentation results on breast cancer masses. An autoregressive model has been employed to filter out of the backscattered RF-signal(More)
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