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MICCAI
2001
Springer

Segmentation of Dynamic N-D Data Sets via Graph Cuts Using Markov Models

12 years 23 days ago
Segmentation of Dynamic N-D Data Sets via Graph Cuts Using Markov Models
Abstract. This paper describes a new segmentation technique for multidimensional dynamic data. One example of such data is a perfusion sequence where a number of 3D MRI volumes shows the dynamics of a contrast agent inside the kidney or heart at end-diastole. We assume that the volumes are registered. If not, we register consecutive volumes via mutual information maximization. The sequence of n registered volumes is regarded as a single volume where each voxel holds an n-dimensional vector of intensities, or intensity curve. Our approach is to segment this volume directly based on voxels intensity curves using a generalization of the graph cut techniques in [7, 2]. These techniques use a spatial Markov model to describe correlations between voxels. Our contribution is in introducing a temporal Markov model to describe the desired dynamic properties of segments. Graph cuts obtain a globally optimal segmentation with the best balance between boundary and regional properties among all seg...
Yuri Boykov, Vivian S. Lee, Henry Rusinek, Ravi Ba
Added 30 Jul 2010
Updated 30 Jul 2010
Type Conference
Year 2001
Where MICCAI
Authors Yuri Boykov, Vivian S. Lee, Henry Rusinek, Ravi Bansal
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