Abstract #1923

Graph-based segmentation of signal voids in time series of diffusion-weighted images of musculature in the human lower leg

Martin Schwartz^1,2, Günter Steidle¹, Petros Martirosian¹, Bin Yang², and Fritz Schick¹

¹Section on Experimental Radiology, Department of Radiology, University of Tuebingen, Tuebingen, Germany, ²Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany

The segmentation of signal voids, which occur in time-series of single-shot diffusion-weighted images, is important for an accelerated evaluation providing larger studies on this phenomenon. The proposed segmentation is based on a two-stage detection and segmentation approach, which utilizes a graph-based representation with random walker optimization. It was demonstrated that the presented method enables a fast and accurate segmentation of signal voids in time-series of diffusion-weighted images.

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