Segmentation of Three-dimensional Electron Tomographic Images
The intuitive understanding of the process of segmentation is that of a compartmentalization of the image into coherent regions and the extraction of independent objects. Perhaps the most sophisticated segmentation mechanism is human vision, which is capable of interpreting a large variety of groups, associating them into classes and compartments, as well as finding relationships among them. Computer-based image segmentation algorithms typically perform only a single task, which is coupled to a specific application. Humans use a large variety of different criteria to segment images, e.g. similarity, proximity, continuity and symmetry. In electron tomography, the observer usually searches for a known shape or multiply occurring shapes to guide his segmentation. The separation criteria used are the gray value and the contrast between the feature and the environment. In a general sense, the aim is to group pixels or voxels into subsets which correspond to meaningful regions or objects.When regarding pictures by eye, one has an intuitive sense for the boundaries of meaningful objects and regions. When using the computer, however, it is difficult to find quantitative criteria which define meaningful areas on the basis of pixel properties such as contours, brightness, color, texture, etc.
KeywordsBinary Image Segmentation Result Manual Segmentation Segmentation Technique Speed Function
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- Ballard, D. H. and Brown, C. M. (1982). Computer Vision. Prentice Hall, Englewood Cliffs, New Jersey.Google Scholar
- Kass, M., Witkin, A. and Terzopoulos, D. (1988). Snakes: active contour models. Int. J. Comput. Vis. 1:362–385.Google Scholar
- Russ, J. C. (2002). The Image Processing Handbook. CRC Press, Boca Raton, Florida.Google Scholar
- Sethian, J. A. (1996). Level Set Methods Evolving Interfaces in Geometry, Fluid Mechanics, Computer Vision, and Materials Science. Cambridge University Press.Google Scholar
- Shi, J. and Malik, J. (1997). Normalized cuts and image segmentation. Paper presented at: Proceedings of the IEEE Conferences on Computer Vision and Pattern Recognition.Google Scholar
- Weickert, J. (1999). Anisotropic Diffusion in Image Processing, B. G. Teubner Stuttgart.Google Scholar