Etienne von Lavante

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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 ultrasound elastography into a segmentation framework. By incorporating(More)
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 this paper a new completely unsupervised mesh segmentation 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)
In the present investigation, the problem of accurate determination of volumetric flows by means of the so-called vortex-shedding flow meter in the case of upstream disturbances caused by several versions of bends has been studied. To this end, the flow about the bluff body used in the presently studied vortex-shedding flow meter was investigated(More)
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