Segmentation of electron tomographic data sets using fuzzy set theory principles.
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 segmentation continues to be the preferred method. Based on previous good experiences using a segmentation based on fuzzy logic principles (fuzzy segmentation) 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 segmentation algorithm evaluating it within the limits of segmenting electron tomograms of selectively stained, plastic embedded spiny dendrites. The results produced by the fuzzy segmentation algorithm within the framework presented are encouraging.
Garduño
Edgar
E
Depto. Ciencias de la Computación, Instituto de Investigaciones en Matermáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Circuito Escolar S/N, Cd. Universitaria, C.P. 04510, Mexico City, Mexico. edgargar@ieee.org
Wong-Barnum
Mona
M
Volkmann
Niels
N
Ellisman
Mark H
MH
D000465:Algorithms; D001185:Artificial Intelligence; D004583:Electrons; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted; D015233:Models, Statistical; D015588:Observer Variation; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012984:Software; D014054:Tomography / Q000295:instrumentation / Q000379:methods*; D014584:User-Computer Interface