Abstract
Computerized tomography using Radon and Fourier Transform has revolutionized medical X-ray imaging and non-destructive examination because of its ability to reconstruct the spatial distribution of X-ray attenuation over cross sections. In geophysical tomography, either electromagnetic energy or seismic energy is used and an iterative reconstruction method has been proposed. In this paper a method of computerized tomography using Neuro-Fuzzy model is proposed. With this method, low contrast pictures of the spatial distribution of attenuation or propagation velocity, whose changes are moderate, can be reconstructed. The Neuro-Fuzzy in this paper is an iterative learning algorithm using fuzzy models consist of Gaussian membership functions. The line integrals of the fuzzy model can be obtained in a simple manner and the spatial distribution is calculated from the line integrals along rays in a plane. This simple formula of the integration of Gaussian radial basis functions is applied to the computerized tomography with relatively small number of propagation paths. A straight-line rayoptics model is assumed for the propagation mechanism. A numerical example of computed tomography by the direct method of solution using the gradient descent method is presented.
