Abstract
An evolutionary algorithm for reconstructing CT gray images from projections is presented; the algorithm reconstructs two-dimensional unknown images from four one-dimensional projections. A Laplacian constraint term is included in the fitness function of the genetic algorithm for handling smooth images, and the evolutionary process reconstructs images into finer ones by partitioning the images gradually thereby increasing the chromosome size exponentially as the generation proceeds. Results obtained are compared to those obtained by the well-known algebraic reconstruction technique (ART), and it was found that the evolutionary method is more effective than ART when the number of projection directions is very limited.
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References
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© 1997 Springer-Verlag Berlin Heidelberg
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Nakao, Z., Takashibu, M., Chen, YW. (1997). Evolutionary CT image reconstruction by image partitioning. In: Yao, X., Kim, JH., Furuhashi, T. (eds) Simulated Evolution and Learning. SEAL 1996. Lecture Notes in Computer Science, vol 1285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0028524
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DOI: https://doi.org/10.1007/BFb0028524
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