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1. THE SEGMENTATION OF 3D IMAGES USING THE RANDOM WALKING
TECHNIQUE ON A RANDOMLY CREATED IMAGE ADJACENCY GRAPH
ABSTRACT
This paper considers the problem of imagesegmentation using the random walker
algorithm. In the caseof 3D images, the method uses an extreme amount of memoryand time
resources. These are required in order to represent thecorresponding enormous image graph and
to solve the resultingsparse linear system. Having in mind these limitations, this paperproposes
techniques for the optimization of the random walkerapproach. The optimization is obtained by
processing supervoxelsrepresenting homogeneous image regions rather than singlevoxels. A fast
and efficient method for supervoxel determinationis introduced. A method for the creation of an
image adjacencygraph from an irregular grid of supervoxels is also proposed.The results of
applying the introduced approach to segmentationof 3D CT data sets are presented and compared
with the resultsof the original random walker approach and other state-of-theartmethods. The
accuracy and the computational overhead is regarded in the comparison. The analysis of results
shows that themodified method can be successfully applied for the segmentationof volumetric
images and provides results in a reasonable timewithout a significant loss in the image
segmentation accuracy.It also outperforms the state-of-the-art methods considered inthe
comparison.