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Relaxation Factor Optimization for Common Iterative Algorithms in Optical Computed Tomography

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  • Wenbo Jiang
  • Xiaohua Zhang

Abstract

Optical computed tomography technique has been widely used in pathological diagnosis and clinical medicine. For most of optical computed tomography algorithms, the relaxation factor plays a very important role in the quality of the reconstruction image. In this paper, the optimal relaxation factors of the ART, MART, and SART algorithms for bimodal asymmetrical and three-peak asymmetrical tested images are analyzed and discussed. Furthermore, the reconstructions with Gaussian noise are also considered to evaluate the antinoise ability of the above three algorithms. The numerical simulation results show that the reconstruction errors and the optimal relaxation factors are greatly influenced by the Gaussian noise. This research will provide a good theoretical foundation and reference value for pathological diagnosis, especially for ophthalmic, dental, breast, cardiovascular, and gastrointestinal diseases.

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  • Wenbo Jiang & Xiaohua Zhang, 2017. "Relaxation Factor Optimization for Common Iterative Algorithms in Optical Computed Tomography," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-8, July.
    • Handle: RePEc:hin:jnlmpe:4850317
    DOI: 10.1155/2017/4850317
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    Cited by:

    1. Omar M. Abou Al-Ola & Ryosuke Kasai & Yusaku Yamaguchi & Takeshi Kojima & Tetsuya Yoshinaga, 2022. "Image Reconstruction Algorithm Using Weighted Mean of Ordered-Subsets EM and MART for Computed Tomography," Mathematics, MDPI, vol. 10(22), pages 1-17, November.

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