### Interactive Organ Segmentation Using Graph Cuts

In *Medical Image Computing and Computer-Assisted Intervention (MICCAI)*,
LNCS 1935, pp. 276-286, Pittsburgh, PA, October 2000.

### Abstract

An N-dimensional image is divided into "object'' and "background''
segments using a graph cut approach. A graph is formed by connecting all
pairs of neighboring image pixels (voxels) by weighted edges. Certain pixels
(voxels) have to be * a priori* identified as object or background
* seeds* providing necessary clues about the image content. Our objective
is to find the cheapest way to cut the edges in the graph so that the object
seeds are completely separated from the background seeds. If the edge cost
is a decreasing function of the local intensity gradient then the minimum cost
cut should produce an object/background segmentation with compact boundaries
along the high intensity gradient values in the image. An efficient, globally
optimal solution is possible via standard min-cut/max-flow algorithms for
graphs with two terminals. We applied this technique to interactively
segment organs in various 2D and 3D medical images.

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