Conclusions

This chapter has described a level set segmentation framework and the preprocessing and data analysis techniques needed for a number of segmentation

Figure 8.26: Sinogram extrapolation results: (a) MIP volume rendering of volume reconstructed from original sinograms, (b) MIP volume rendering of volume reconstructed from augmented (extrapolated) sinograms, (c) a portion of original MIP enlarged, and (d) the corresponding portion in augmented MIP enlarged.

Figure 8.26: Sinogram extrapolation results: (a) MIP volume rendering of volume reconstructed from original sinograms, (b) MIP volume rendering of volume reconstructed from augmented (extrapolated) sinograms, (c) a portion of original MIP enlarged, and (d) the corresponding portion in augmented MIP enlarged.

applications. Several standard volume processing algorithms have been incorporated into the framework in order to segment datasets generated from MRI, CT, and TEM scans. A technique based on moving least-squares has been developed for segmenting multiple nonuniform scans of a single object. New scalar measures have been defined for extracting structures from diffusion tensor MRI scans. Finally, a direct approach to the segmentation of incomplete tomographic data using density parameter estimation is described. These techniques, combined with level set surface deformations, allow us to segment many different types of biological volume datasets.

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