Image reconstruction by minimizing second-order moment.
Statistical methods have received much attention in image reconstruction, especially when the projection data are not sufficient in number, since they usually give more reliable results in such a case than other methods. In this paper, we propose a new statistical method which minimizes the second-order moment of an object that indicates the variance and randomness in a statistical structure. By minimizing the second-order moment, the resulting image reflects the statistical structure imposed by the available information and is biased toward a flat gray structure in the absence of information. The computer simulation results show a good convergence behavior of the proposed method. This method was successfully applied to ultrasound attenuation CT using a sponge phantom.
Park
K H
KH
Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Seoul.
Park
S B
SB
D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D014057:Tomography, X-Ray Computed; D014465:Ultrasonics