Segmentation of two- and three-dimensional data from electron microscopy using eigenvector analysis.
An automatic image segmentation method is used to improve processing and visualization of data obtained by electron microscopy. Exploiting affinity criteria between pixels, e.g., proximity and gray level similarity, in conjunction with an eigenvector analysis, the image is subdivided into areas which correspond to objects or meaningful regions. Extending a proposal by Shi and Malik (1997, Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 731-737) the approach was adapted to the field of electron microscopy, especially to three-dimensional application as needed by electron tomography. Theory, implementation, parameter setting, and results obtained with a variety of data are presented and discussed. The method turns out to be a powerful tool for visualization with the potential for further improvement by developing and tuning new affinity.
Frangakis
Achilleas S
AS
Max-Planck-Institut für Biochemie, Am Klopferspitz 18a, D-8215 Martinsried, Germany.
Hegerl
Reiner
R
D000465:Algorithms; D019620:Desulfurococcaceae / Q000166:cytology / Q000648:ultrastructure; D007091:Image Processing, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008854:Microscopy, Electron / Q000379:methods*; D008962:Models, Theoretical; D017906:Siphoviridae / Q000648:ultrastructure; D014057:Tomography, X-Ray Computed