Weights and Topology: A Study of the Effects of Graph Construction on 3D Image Segmentation

Authors:
Leo Grady;Marie-Pierre Jolly
Affiliations:
Siemens Corporate Research -- Dept. of Imaging and Visualization, , Princeton, NJ 08540;Siemens Corporate Research -- Dept. of Imaging and Visualization, , Princeton, NJ 08540
Venue:
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Year:
2008

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Abstract

Graph-based algorithms have become increasingly popular for medical image segmentation. The fundamental process for each of these algorithms is to use the image content to generate a set of weights for the graph and then set conditions for an optimal partition of the graph with respect to these weights. To date, the heuristics used for generating the weighted graphs from image intensities have largely been ignored, while the primary focus of attention has been on the details of providing the partitioning conditions. In this paper we empirically study the effects of graph connectivity and weighting function on the quality of the segmentation results. To control for algorithm-specific effects, we employ both the Graph Cuts and Random Walker algorithms in our experiments.