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...

• Type: Conference
• Year: 2001
• Where: MICCAI
• Authors: Yuri Boykov, Vivian S. Lee, Henry Rusinek, Ravi Bansal
• Keywords: Globally Optimal, Intensity Curves, Medical Imaging, MICCAI 2001, Perfusion Sequences