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

Y. Boykov, V.S. Lee, H. Rusinek, R. Bansal

In Medical Image Computing and Computer-Assisted Intervention (MICCAI), LNCS 2208, pp. 1058-1066, Utrecht, The Neverlands, October 2001.


This paper describes a new segmentation technique for multi-dimensional 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 [Greig:JRSSB89,BJ:01]. 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 segmentations satisfying user placed hard constraints. Flexibility, coherent theoretical formulation, and the possibility of a globally optimal solution are attractive features of our method that gracefully handles even low quality data. We demonstrate results for 3D kidney and 2D heart perfusion sequences.

WHOLE PAPER: pdf file (177 Kb)