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## Computational issues

Of course, moving to 3D also greatly increases the computational complexity and expense of the image segmentation process. Lomask (2007) describes several modifications to the algorithm that help to lessen the impact, such as comparing each pixel to a random selection of other pixels instead of all pixels in a specified neighborhood. However, constant technological advances in the computer hardware industry also contribute to the increasing tractability of large-scale computational problems such as this one. The segmentation algorithm used here involves heavy computations with very large, sparse matrices. As such, a promising avenue of interest is to work with many-core, large-memory machines such as those recently developed by SiCortex (Reilly et al., 2006). Because such machines feature very fast interprocessor communication capabilities, they lend themselves well to the sparse-matrix eigenvector calculation portion of the segmentation scheme that, in most cases, represents the majority of overall computational expense. Early implementations of the eigenvector calculation algorithm on a SiCortex development machine with 72 low-power, relatively low-performance nodes bear out this hypothesis. The matrix-vector multiplications needed for calculation of the eigenvector on a 250 x 400 x 50 cube of data required approximately four minutes on this machine, representing a speedup of over 750% when compared to the same calculations on a single processor with much higher relative speed and power consumption. Optimization of codes to take greater advantage of the machine's capabilities should further improve these results.

Next: Attribute combinations in 3D Up: Segmentation in three dimensions Previous: Segmentation in three dimensions