A parallel technique for three-dimensional image segmentation

The authors present a parallel 3-D image segmentation algorithm which, through the use of α-partitioning and volume filtering, segments 3-D images such that the gray-level variation within each volume can be described by a regression model. Experimental results indicate that the use of discontinuity locations in α-partitioning can greatly assist the segmentation of 3-D images. It is also shown that the use of volume filters in removing incorrect partitions permits quick segmentation of 3-D images into functional descriptions for each volume. These two characteristics enabled the parallel 3-D image segmentation algorithm to produce fair segmentation results with large savings in computation time