Segmentation of Dynamic N-D Data Sets via Graph Cuts Using Markov Models (original) (raw)

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 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.

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Author information

Authors and Affiliations

  1. Siemens Corporate Research, Imaging & Visualization, Princeton, NJ, 08540, USA
    Yuri Boykov & Ravi Bansal
  2. NYU School of Medicine, Radiology, 550 First Avenue, New York, NY, 10016, USA
    Vivian S. Lee & Henry Rusinek

Authors

  1. Yuri Boykov
  2. Vivian S. Lee
  3. Henry Rusinek
  4. Ravi Bansal

Editor information

Editors and Affiliations

  1. Image Sciences Institute, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
    Wiro J. Niessen & Max A. Viergever &

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© 2001 Springer-Verlag Berlin Heidelberg

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Boykov, Y., Lee, V.S., Rusinek, H., Bansal, R. (2001). Segmentation of Dynamic N-D Data Sets via Graph Cuts Using Markov Models. In: Niessen, W.J., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2001. MICCAI 2001. Lecture Notes in Computer Science, vol 2208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45468-3\_126

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