Garduño E, Wong-Barnum M, Volkmann N, Ellisman MH. Segmentation of electron tomographic data sets using fuzzy set theory principles. J Struct Biol. 2008 Jun;162(3):368-79.
In electron tomography the reconstructed density function is typically corrupted by noise and artifacts. Under those conditions, separating the meaningful regions of the reconstructed density function is not trivial. Despite development efforts that specifically target electron tomography manual previous termsegmentationnext term continues to be the preferred method. Based on previous good experiences using a previous termsegmentationnext term based on fuzzy logic principles (fuzzy previous termsegmentationnext term) where the reconstructed density functions also have low signal-to-noise ratio, we applied it to electron tomographic reconstructions. We demonstrate the usefulness of the fuzzy previous termsegmentationnext term algorithm evaluating it within the limits of segmenting electron tomograms of selectively stained, plastic embedded spiny dendrites. The results produced by the fuzzy previous termsegmentationnext term algorithm within the framework presented are encouraging.